Upload
others
View
4
Download
0
Embed Size (px)
Citation preview
Real Effects of the Sovereign Debt Crisis in Europe:
Evidence from Syndicated Loans
Viral V. Acharya, Tim Eisert, Christian Eufinger, and Christian Hirsch∗
November 7, 2015
ABSTRACT
We explore the impact of the European Sovereign Debt Crisis and the resulting credit crunch
on the corporate policies of firms. We show that banks’ exposures to impaired sovereign debt
and the risk-shifting behavior of undercapitalized banks are two important contributors to
the negative real effects suffered by European firms. In particular, we present firm-level
evidence showing that the lending contraction of banks affected by the crisis depresses the
investment, job creation, and sales growth of the firms with significant business relationships
to these banks. During the crisis, these firms show behavior typical for financially constrained
firms.
∗Acharya is with New York University, CEPR, and NBER. Eisert is with Erasmus University Rotterdam.Eufinger is with the IESE Business School. Hirsch is with Goethe University Frankfurt and SAFE. Wethank our discussants Heitor Almeida, Nelson Camanho, Daniela Fabbri, Jhangkai Huang, Yi Huang, VassoIoannidou, Victoria Ivashina, Anil Kashyap, Francesco Manaresi, and Andrea Presbitero. Moreover, weappreciate helpful comments from Bo Becker, Matteo Crosignani, Giovanni Dell’Ariccia, Miguel Ferreira,Rainer Haselmann, Augustin Landier, Tatyana Marchuk, Steven Ongena, Marco Pagano, Sjoerd van Bekkum,and Annette Vissing-Jorgensen. Furthermore, we thank conference participants at the 2015 NBER SI,EFA Meeting 2014, the Sovereign Debt Conference at Nova, the CSEF conference on “Bank Performance,Financial Stability and the Real Economy”, the RELTIF CEPR Meeting Oxford 2015, the ESCB Day aheadconference, the 4th MoFiR workshop on banking, the International Conference on “Financial Market Reformand Regulation”, and the Tsinghua Finance Workshop 2014, as well as seminar participants at Berkeley,Harvard, Boston College, NYU, Columbia, Duke, Amherst, Temple, BI Oslo, Zurich, Trinity College Dublin,IESE, the European Central Bank, CUNY, Mainz, and Konstanz.
Starting in 2009, countries in the periphery of the eurozone drifted into a severe sovereign
debt crisis as concerns about the deterioration of credit quality made it increasingly difficult
for the affected countries to refinance and service existing debt. Since the deterioration in
the sovereigns’ creditworthiness fed back into the financial sector (Acharya et al. (2014b);
Acharya and Steffen (2015)), lending to the private sector contracted substantially in Greece,
Ireland, Italy, Portugal, and Spain (the GIIPS countries). For example, in Ireland, Spain, and
Portugal, the overall lending volume of newly issued loans fell by 82%, 66%, and 45% over
the 2008-2013 period, respectively.1 This loan supply contraction led to a sharp increase
in the uncertainty for borrowing firms as to whether they would be able to secure bank
funding in the future. As Pietro Fattorini, the owner and manager of a 23-year-old Italian
company puts it: “It’s like starting to drive on the motorway without knowing if you’ll find
gas stations on the way.”2
This statement suggests that the contraction in bank lending negatively affected the
corporate policies of firms and thus might have been an important contributor to the severity
of the European Sovereign Debt Crisis. However, there is still no conclusive evidence as to
(i) how important the bank lending channel was to the severity of the crisis as opposed to
the overall macroeconomic shock; (ii) whether the credit crunch had any real effects for the
borrowing firms in Europe since firms facing a withdrawal of credit from one financing source
might have been able to get funding from another source (Adrian et al. (2013); Becker and
Ivashina (2014a)); and (iii) what actually caused the tightening in bank lending.
Against this background, our paper makes two important contributions to the literature.
First, we show that the decline in bank lending during the European Sovereign Debt Crisis
was indeed an important contributor to the severity of the crisis. In particular, we present
firm-level evidence that the loan supply contraction of banks affected by the sovereign debt
crisis made firms with a higher dependence on these banks financially constrained. As a result
of the limited access to bank financing, we show that firms affiliated with banks affected by
the crisis encountered strong negative real outcomes; their investments, employment growth,
and sales growth faltered. Our estimates suggest that the credit crunch explains between
one-fifth and one-half of the overall negative real effects in the sample.
1“SMEs in peripheral Eurozone face far steeper borrowing rates” by Patrick Jenkins, Financial Times,October 10, 2013.
2“Italian Banks’ Woes Hurt Small Firms” by Giovanni Legorano, The Wall Street Journal, December 1,2013.
2
Second, to the best of our knowledge, we are the first to explore the complex channels
through which the European Sovereign Debt Crisis induced a reduction in bank lending,
as well as the associated negative real effects for borrowing firms. We document that the
negative real effects of the debt crisis that can be attributed to the bank lending channel are
primarily associated with (i) banks from GIIPS countries facing increased risk of losses on
their significant domestic sovereign bondholdings, and (ii) the resulting incentive of under-
capitalized banks from GIIPS countries to engage in risk-shifting behavior by buying even
more domestic sovereign bonds, thereby crowding out corporate lending.
Our sample is based on loan information data obtained from Thomson Reuters LPC’s
DealScan, which provides extensive coverage of bank-firm relationships throughout Europe.
We augment this dataset by hand-matching firm-specific information from Bureau van Dijk’s
Amadeus database and bank-specific information from various sources. The sample includes
firms from all European countries that were severely affected by the sovereign debt crisis (the
GIIPS countries) and firms incorporated in Germany, France, and the U.K. (the non-GIIPS
countries), which are the countries with the largest number of syndicated loans among the
European countries that were not significantly affected by the sovereign debt crisis. Our
sample period covers the 2006–2012 period.
Our dataset provides three key advantages for studying the economic impact of the
sovereign debt crisis and the resulting lending supply contraction on European firms. First,
the fact that the sample is pan-European and includes a geographical breakdown of the firms’
subsidiary revenues enables us to more precisely disentangle the adverse effects on the real
economy caused by the macroeconomic demand and the bank credit supply shock. Second,
our sample enables us to rule out the possibility that a reduction in bank lending by domestic
banks is substituted by bank credit from foreign financial institutions. Third, and most
importantly, the bank-specific information together with data on bank-firm relationships
allows us to determine which channels drive the contraction in bank lending, and thus cause
the negative real effects for borrowing firms.
There are at least three potential channels through which the sovereign debt crisis might
have affected bank lending and, in turn, the corporate policies of borrowing firms: one passive
and two active. The passive channel is the hit on a bank’s balance sheet. The active channels
are risk-shifting and moral suasion. The passive channel works through the dramatic increase
in the risk of GIIPS sovereign debt, which directly translated into losses for banks due to
3
their large sovereign bondholdings, as shown by the recent European Banking Authority’s
(EBA’s) EU-wide stress tests and capital exercises. To cope with these losses, banks had to
deleverage and thus might have reduced lending to the private sector (e.g., see Bocola (2014)
for a theoretical model of this mechanism).
In the first active channel, the risk-shifting motive arises since weakly-capitalized banks
from GIIPS countries might have had incentives to increase their risky domestic sovereign
bondholdings even further. This asset class offers a relatively high return and at the same
time has a very high correlation with the banks’ portfolio (Diamond and Rajan (2011);
Crosignani (2014)). The latter is important since a proper “risk-shifting asset” only generates
large losses in states of the world in which the bank is in default anyway, which is true
for domestic sovereign debt as European banks usually have large domestic government
debt holdings (in the case of GIIPS banks often exceeding 100% of their core capital).
In addition, eurozone regulators consider these bonds to be risk-free (i.e., attach zero risk
weights) and removed the concentration limits for sovereign debt exposures, which allows
large bets without having to provide equity capital. This risk-shifting mechanism might
have led to a crowding-out of lending to the private sector and thus might have negatively
impacted the real economy.
In the second active channel, according to the moral suasion motive, a government might
have explicitly or implicitly pressured domestic banks to increase their domestic sovereign
bondholdings in case it found it difficult to refinance its debt (e.g., Becker and Ivashina
(2014b)), which also might have crowded out lending to the real sector.
To assess whether the European Sovereign Debt Crisis affected the real economy in
Europe through the bank lending channel, we start by taking into account all potential
bank lending channels (i.e., balance sheet hit, risk-shifting, and moral suasion) by using a
bank’s country of incorporation as a proxy for how strongly it was affected by the crisis. All
three channels are related to the banks’ country of incorporation as (i) banks’ generally have
large domestic sovereign bondholdings, implying a large exposure to domestic sovereign risk
(balance sheet hit channel) and (ii) banks might willingly or due to government pressure
increase their domestic sovereign debt holdings even further, which potentially crowds out
corporate lending (risk-shifting and moral suasion channels).
Based on a bank’s country of incorporation, we divide banks into two groups: (i) GIIPS
banks, which are banks headquartered in GIIPS countries, and (ii) non-GIIPS banks, that
4
is, banks from Germany, France, and the U.K. To consistently estimate the real effects for
borrowing firms having pre-crisis relationships with banks affected by the sovereign debt
crisis, in our main specification we compare the change in the corporate policies after the be-
ginning of the crisis across firms from the same country and industry but which differ in their
dependence on GIIPS banks. In particular, we include industry-country-year fixed effects to
capture any time-varying shocks to an industry in a given country that may have affected
the credit demand of borrowing firms, their access to credit, and/or their real outcomes.
Moreover, we include foreign bank country-year fixed effects to absorb any unobserved,
time-varying heterogeneity that may arise because a firm’s dependency on banks from a cer-
tain country might be influenced by whether this firm has business in the respective country.
Consider as an example a German firm borrowing from a Spanish bank and a German bank.
For this firm, we also include a Spain-year fixed effect to capture the firm’s potential expo-
sure to the macroeconomic downturn in Spain during the sovereign crisis. Furthermore, we
control for unobserved, time-constant firm heterogeneity and observable time-varying firm
characteristics that affect the firms’ corporate policies, loan demand, and/or loan supply.
Our results document that during the sovereign debt crisis, firms with a high depen-
dence on banks incorporated in GIIPS countries have exhibited behavior that is typical for
financially constrained firms. That is, they had lower interest coverage ratios and leverage,
have demonstrated a significantly positive propensity to save cash out of their cash flows,
and have relied more on cash relative to bank lines of credit for their liquidity management.
These results are not observed for firms that are not dependent on GIIPS banks, nor for
highly GIIPS bank-dependent firms in the pre-crisis period prior. We then explore how these
financially constrained firms adjusted their corporate policies. We find that firms that had
significant business relationships with GIIPS banks decreased investment more, and expe-
rienced less job creation and sales growth compared to firms that were less dependent on
GIIPS banks.
These findings do not seem to be driven by how firms and banks formed business rela-
tionships in the pre-crisis period. Comparing firms with high and low dependency on GIIPS
banks suggests that firms in the two groups are comparable in terms of the outcome variables
and other observable dimensions in the pre-crisis period, confirming that the parallel trend
assumption holds. Furthermore, there were no significant pre-crisis differences between GI-
IPS and non-GIIPS banks that could explain our results. Lastly, we can rule out that loan
5
syndicates that include GIIPS banks were of lower quality in the pre-crisis period.
To check the robustness of our results, we alternatively identify the real effects caused
by the decrease in loan supply by tracking the change in the corporate policies of firms that
are not directly affected by the macroeconomic shock in the periphery of the eurozone or
any other part of the world. In particular, we focus our analysis on non-GIIPS firms that
had a pre-crisis relationships with GIIPS banks, but do not have business exposure to GIIPS
or other non-EU countries.3 To this end, we collect revenue information of all foreign and
domestic subsidiaries of the firms in our sample. Furthermore, to rule out that a firm’s
dependency on GIIPS banks is positively correlated with its non-observed business exposure
to GIIPS countries, we only consider non-GIIPS firms for which the GIIPS bank relationships
are due to reasons unrelated to the geographical distribution of the firms’ business exposure.
In particular, we only consider firms that inherited their relationship with a GIIPS bank
through bank mergers or acquisitions or which had a lending relationship to a foreign bank
that has historically had a large presence in the respective country.4 All results continue to
hold for this alternative identification strategy, confirming that the bank lending channel was
an important contributor to the negative real effects for borrowing firms during the sovereign
debt crisis. In addition, this result shows that even firms that were not directly affected by the
crisis had to face indirect consequences if they had strong ties to banks that were affected by
the sovereign debt crisis. This finding thus highlights that the extensive cross-border lending
relations in Europe can amplify the shock transmission across the eurozone.
To ensure that the negative real effects are caused by a loan supply reduction, we analyze
whether the impact of having a connection to GIIPS banks was less pronounced for firms
that were either highly likely able to obtain financing from another source or for which the
loan supply tightening did not lead to a financing shortage as they recorded an even larger
loan demand decrease. Indeed, we only find significant real effects that can be attributed to
banks’ lending behavior for firms that were unlikely able to tap alternative funding sources,
that is, non-listed firms, unrated firms, and firms that were not able to switch banks or issue
bonds. Furthermore, we find that firms with higher exposure to the macroeconomic shock in
the European periphery (thus relative low loan demand) suffered less real effects through the
3For example, a German company without significant business activity in GIIPS or non-EU countriesthat had a pre-crisis lending relationship with a Spanish bank.
4Roughly 90% of lending relationships between non-GIIPS firms without subsidiaries in GIIPS or othernon-EU countries and GIIPS banks can be explained by these two reasons.
6
bank lending channel compared to firms that had less or no business exposure to the affected
regions (thus relative high loan demand). These results again confirm that the limited access
to funding due to lending relationships with banks affected by the European Sovereign Debt
Crisis played a major role in inducing negative real effects for the affected borrowing firms.
We use a partial equilibrium analysis to quantify the importance of the credit supply
shock. By estimating the counterfactual real outcome if a firm had a lower exposure to
affected banks, we can get an estimate of the magnitude of the real effects that were due to
the loan supply disruptions of GIIPS banks. Our results suggest that in the case of GIIPS
firms, between one-third and one-half of the overall negative real effects in our sample can be
attributed to banks’ lending behavior. For non-GIIPS firms, we can explain between one-fifth
to one-quarter of the aggregate reduction in the real outcome variables. Not surprisingly,
we can explain less of the overall evolution for non-GIIPS firms since many borrowers in
non-GIIPS countries have no exposure to GIIPS banks.
Given that firms that had a pre-crisis lending relationship with a bank affected by the
European Sovereign Debt Crisis suffered significant negative real effects, we then test what
actually caused the bank lending contraction and ultimately the negative real effects for
borrowing firms. To this end, we determine for each bank in our sample to what degree
it was “affected” by the crisis, where affected is defined, in line with the three potential
channels through which the crisis might have affected bank lending, as having (i) an above
median exposure to sovereign risk (balance sheet hit), (ii) a below median capitalization or
rating (risk-shifting), or (iii) an above median influence of governments (moral suasion).
To collect evidence for the hit on the balance sheet channel, we use data from the EBA’s
EU-wide stress tests and capital exercises and calculate each bank’s exposure to the sovereign
debt crisis. Furthermore, we obtain information about the banks’ health from SNL Financial
(leverage) and Bloomberg (ratings) to analyze whether GIIPS banks with low capital buffers
engaged in risk-shifting by buying additional domestic sovereign debt and cutting corporate
lending. Finally, we use data about government interventions, government bank ownership,
and government board seats to measure the influence of governments on their domestic banks
and test whether real effects can also be attributed to the moral suasion channel.
Both active channels, the risk-shifting and the moral suasion channel, are consistent
with an increase in domestic sovereign bondholdings over the crisis period, which makes
their disentanglement challenging. Therefore, we first explore whether banks changed their
7
sovereign debt holdings after the outbreak of the European Sovereign Debt Crisis. We
find that weakly-capitalized GIIPS banks significantly increased their holdings of domestic
sovereign debt, whereas we do not find a statistically significant relationship between our
moral suasion proxies and the propensity of banks to buy additional domestic sovereign debt.
This indicates that risk-shifting played a more important role for the cutback in lending.
To formally test the importance of the different channels for the reduction in bank lend-
ing, we apply a modified version of the Khwaja and Mian (2008) estimator, which exploits
multiple bank-firm relationships before and during the sovereign debt crisis to control for
loan demand and other observed and unobserved borrowing firm characteristics. However,
since syndicated loans usually have relatively long maturities and we do not observe changes
within the same loan over time (e.g., credit line drawdowns), a large number of observations
in our sample have no significant year-to-year change in the bank-firm lending relationships.
Therefore, we have to aggregate firms into clusters to generate enough time series hetero-
geneity in bank lending, which then allows us to control for observed and unobserved firm
characteristics that are shared by firms in the same cluster. In particular, we form firm
clusters based on the country of incorporation, the industry, and the firm rating.
Our results show that banks with higher sovereign risk in their portfolios tightened lending
more and charged higher loan spreads in the crisis period than banks with lower sovereign risk
exposures. Furthermore, the findings show that weakly-capitalized GIIPS banks cut their
lending more and charged higher spreads than well-capitalized GIIPS banks, irrespective
of whether risk-shifting incentives are proxied with leverage or rating. With regard to the
moral suasion channel, none of the three proxies indicates that moral suasion influenced
bank lending during the sovereign debt crisis.
We next examine whether these channels also played an important role in causing the real
effects experience by borrowing firms. In line with our bank lending regressions, our results
confirm that the negative real effects of the sovereign debt crisis that can be attributed to
the bank lending channel are mainly due to the hit on banks’ balance sheets (resulting from
their large sovereign debt holdings) and their incentive to engage in risk-shifting behavior
(i.e., buying more risky sovereign bonds).
In summary, we shed light on the complex interaction between bank and sovereign health
and its impact on the real economy. In particular, we show that there are significant spillovers
from periphery sovereigns to the local real economy, as well as cross-border spillovers to firms
8
in non-GIIPS countries that are transmitted through the bank lending channel. Therefore,
while the eurozone greatly benefits its members by deepening the degree of financial integra-
tion, we document that cross-border bank lending also can facilitate the shock transmission.
In particular, when the banking sector experiences an aggregate shock like the periphery
sovereign debt crisis and it is not recapitalized.
I. Related Literature
In general, our paper contributes to the literature on how shocks on banks’ liquidity
or solvency are transmitted to the real economy. Starting with Bernanke (1983), several
researchers have taken on this theme.5
In particular, our paper adds to the literature on the impact of the European Sovereign
Debt Crisis on bank lending. Existing theory suggests that sovereign crises can affect the
real economy through several channels in complex ways based on the nature of the inter-
action between bank and sovereign health. According to Acharya et al. (2014b), distress
in the financial sector might induce governments to bailout weak banks, which, in turn,
increases sovereign credit risk. An increase in sovereign risk, however, lowers the value of
both government guarantees and the banks’ bondholdings, thereby again weakening the fi-
nancial sector. Bocola (2014) shows that higher sovereign risk not only tightens the banks’
funding constraints, but also raises the risks associated with lending to the corporate sector,
both of which lead to a decrease in the credit supply. Farhi and Tirole (2014) allow in their
model for both sovereign debt forgiveness and financial sector bailouts. In this setting, banks
might have an incentive to engage in collective risk-shifting by buying domestic bonds, which
might not be prohibited by their domestic governments if there is a possibility of sovereign
debt forgiveness. Uhlig (2014) shows that governments in risky countries have an incentive
to allow their banks to load up on domestic sovereign debt if these bonds can be used for
repurchase agreements with a common central bank.
Regarding the empirical evidence, De Marco (2014) and Popov and Van Horen (2014)
find that after the outbreak of the European Sovereign Debt Crisis, non-GIIPS European
banks with significant exposures to GIIPS sovereign bonds reduced lending and increased
5For a comprehensive overview over the “natural experiment” literature on shocks that induce variationin the cross-section of credit availability, see Chodorow-Reich (2014).
9
loan rates more than non-exposed banks. Similar to our study, De Marco (2014) and Popov
and Van Horen (2014) also use data on syndicated lending. Bofondi et al. (2013) confirm this
finding using bank-firm matches from the Bank of Italy’s Credit Register. Finally, Becker and
Ivashina (2014b) conclude that banks shifting from firm lending to increasing their domestic
sovereign bondholdings is aggravated by the moral suasion of European governments. These
studies, however, neither analyze the consequences of the contraction in bank lending during
the sovereign debt crisis for the real economy, nor determine which channels actually cause
the significant negative real effects.
Most importantly, our paper adds to the natural experiment literature on the real effects
of bank lending supply shocks at the firm-level, which is a challenging task as it requires
data on bank-firm relationships, as well as firm-level information. Therefore, there have only
been a few papers addressing this research question. Regarding the recent 2008-09 financial
crisis, Chodorow-Reich (2014) uses the DealScan database and employment data from the
U.S. Bureau of Labor Statistics Longitudinal Database to show that firms that had pre-
crisis relationships with banks that struggled during the crisis reduced employment more
than firms that had relationships with healthier lenders. Similarly, Bentolila et al. (2013)
match employment data from the Iberian Balance Sheet Analysis System and loan informa-
tion obtained from the Bank of Spain’s Central Credit Register to document that during
the recent financial crisis, Spanish firms that had relationships with banks that obtained
government assistance recorded a higher job elimination than firms with relationships with
healthy banks. Finally, Cingano et al. (2013) use the Bank of Italy’s Credit Register to pro-
vide evidence that firms which borrowed from banks with a higher exposure to the interbank
market experienced a larger drop in investment and employment levels in the aftermath of
the recent financial crisis.
However, the impact of sovereign debt crisis on bank lending is much more complex com-
pared to the bank lending supply shock caused by the 2008-09 financial crisis, which mainly
impaired the banks’ financial health. As shown by the theoretical literature (e.g., (Diamond
and Rajan (2011); Crosignani (2014)), aside from its impact on bank health, a sovereign debt
crisis might additionally lead to a crowding-out of corporate lending as it creates incentives
for banks to increase their risky domestic sovereign bondholdings. Moreover, governments
might pressure domestic banks to buy even more domestic sovereign debt, which might also
crowd out lending. To our knowledge, our paper and a concurrent paper by Balduzzi et al.
10
(2014) are the only papers that investigate the real effects of the European Sovereign Debt
Crisis. Using survey data on micro and small Italian firms, Balduzzi et al. (2014) find that
firms with connections to banks with high CDS spreads invest less, hire fewer workers, and
reduce borrowing. In contrast, we use data from syndicated loans, which are mainly used by
large corporations. Therefore, our estimates serve as a lower bound for the adverse effects
of the bank credit supply shock in Europe, since these effects are supposedly even more
pronounced for smaller firms given their inability to find alternative funding sources.
Our paper is the first to shed light on the question through which channels the European
Sovereign Debt Crisis actually caused a contraction in bank lending and the resulting real
effects for borrowing firms. In particular, we document that the negative real effects of the
sovereign debt crisis are due to both risk-shifting behavior and a reduction in bank health
from exposures to impaired sovereign debt.
II. Data
We use a novel hand-matched dataset that contains bank-firm relationships in Europe,
along with detailed firm and bank-specific information. Information about bank-firm re-
lationships are from Thomson Reuters LPC’s DealScan, which provides a comprehensive
coverage of the syndicated loan market. In Europe, bank financing is the key funding source
for firms, as banks provide more than 70% of debt for European firms and only very few bonds
are issued in Europe (see Standard&Poor’s (2010) and Dombret and Kenadjian (2015)).
Syndicated loans are an important financing source for European non-financial corpora-
tions as on average between 2005 and 2009 roughly 20% of all extended loans to these firms
were syndicated loans.6 We collect information on syndicated loans to non-financial firms
from all GIIPS countries. In addition, to be better able to disentangle the macro and bank
lending supply shock, we include in our sample firms incorporated in Germany, France, and
U.K. (non-GIIPS countries), which are the countries with the largest number of syndicated
loans among the European countries that were not significantly affected by the sovereign
debt crisis. Consistent with the literature (e.g., Sufi (2007)), all loans are aggregated to
6Figure A1 in the Online Appendix shows the fraction of syndicated loans relative to the total amountof loans issued to non-financial corporations in a given country, measured as the average fraction for the2005–2009 period.
11
a bank’s parent company. Our sample period is from 2006 to 2012, such that we have a
symmetric time window surrounding the beginning of the European Sovereign Debt Crisis.
We augment the data on bank-firm relationships with firm-level data taken from Bureau
van Dijk’s Amadeus database. This database contains information on 19 million public and
private companies from 34 countries, including all EU countries. DealScan and Amadeus do
not share a common identifier. To merge the information in these databases, we hand-match
firms to the DealScan database. Amadeus groups firms into different size categories ranging
from “small” to “very large”. Perhaps not surprisingly, firms in the intersection of Amadeus
and DealScan are either classified as “large” or “very large”. For firms to be classified as
large, they have to satisfy at least one of the following criteria: operating revenue of at
least e10 million, total assets of at least e20 million, at least 150 employees, or be publicly
listed. The respective criteria for very large companies are: at least e100 million operating
revenue, at least e200 million total assets, or at least 1,000 employees. Table A1 in the
Online Appendix reports the results of a comparison of firms in the intersection of Amadeus
and DealScan and the remaining firms from GIIPS countries and Germany, France, and
U.K. in the category of “very large” in Amadeus. The comparison shows that the firms in
our sample are on average larger and have a higher ratio of tangible to total assets, but are
comparable along other firm characteristics. Furthermore, we hand-match our sample to the
Capital IQ database to obtain detailed data on the whole debt structure for a subsample of
our firms, including detailed information on total outstanding and undrawn credit lines.
In addition, we augment the dataset with bank-level information from various sources.
We retrieve data about the sovereign debt holdings of European banks from the EBA’s
EU-wide stress tests and capital exercises. Furthermore, we obtain information about the
banks’ health from SNL Financial (leverage) and Bloomberg (ratings). To get data about
governmental influence on European banks, we obtain data about government interventions
compiled from information disclosed on the official EU state-aid websites.7 Finally, we com-
pile government bank ownership data from Bankscope, and extract the fraction of directors
affiliated with the respective government from the BoardEx database. The definitions of all
variables are summarized in Table I.
7The data can be obtained from: http://ec.europa.eu/competition/elojade/isef/index.cfm?
clear=1&policy_area_id=3.
12
III. Financial and Real Effects of the European
Sovereign Debt Crisis
Our objective is to examine the association between a bank’s exposure to the European
Sovereign Debt Crisis and the resulting corporate policy of its borrowing firms. We expect
that firms that are more dependent on banks significantly affected by the sovereign debt
crisis were more financially constrained during the crisis and thus acted differently both in
terms of financial and real decisions compared to less affected firms.
A. Methodology
We start with broadly assessing whether the European Sovereign Debt Crisis affected
the real economy through the bank lending channel. Therefore, to first capture all channels
through which banks were affected, we use a bank’s country of incorporation as a measure
for its exposure to the sovereign debt crisis. The bank’s country of incorporation is a good
“catch-all” measure as (i) banks’ bond portfolios are generally biased towards domestic
sovereign bondholdings, implying that there is a strong positive relation between a bank’s
country of incorporation and its exposure to the sovereign debt of that country (hit on
balance sheet); (ii) GIIPS banks have an incentive to buy additional risky domestic debt
(risk-shifting); and (iii) GIIPS governments potentially pressure domestic banks to increase
their domestic sovereign bondholdings (moral suasion). All three channels could potentially
lead to a reduction in the corporate loan supply, either by reducing a bank’s debt capacity (hit
on balance sheet), or by crowding-out corporate lending (risk-shifting and moral suasion).
In Section IV, we provide a more detailed explanation of the three channels and analyze
which of these channels are of first-order importance for the negative real effects incurred by
the borrowing firms.
For the analysis, we divide banks into two groups: (i) GIIPS banks, which are banks head-
quartered in GIIPS countries given that these countries are most affected by the sovereign
debt crisis and (ii) non-GIIPS banks, that is, banks from Germany, France, and the U.K.,
which are the countries with the largest number of syndicated loans among the European
countries that were not significantly affected by the sovereign debt crisis. To measure a
firm’s dependency on GIIPS banks in a given year, we determine the fraction of the firm’s
13
total outstanding syndicated loans that is provided by GIIPS lead arrangers. Therefore, the
GIIPS Bank Dependence of firm i in country j, and industry h in year t is defined as:
GIIPS Bank Dep.ijht =
∑l∈Lijh,min{t,ti}
Φl
#Lead Arranger l
· Loan Amount l
Total Loan Amount ijh,min{t,ti}, (1)
where Φl =∑
b∈l GIIPS b and Lijht are all of firm i’s loans outstanding at time t. GIIPS b is
a dummy variable that indicates whether lead arranger bank b is incorporated in a GIIPS
country, in which case it is equal to one and otherwise zero. Hence, Φl counts the number
of GIIPS lead arranger banks in the syndicated loan l, while #Lead Arranger l is the total
number of lead arrangers in loan l. Furthermore, ti refers to the last year in which none of
firm i’s banks entered the respective crisis period yet. We keep the GIIPS Bank Dependence
constant at its pre-sovereign debt crisis level for each crisis year to address the concern that
firms with bad performance during the crisis lost the opportunity to get funding from non-
GIIPS banks and thus could only rely on GIIPS banks.8 Otherwise, our results could be
biased since badly performing firms then would have been more likely to have a higher GIIPS
Bank Dependence, and we could not attribute the effects we find to the credit crunch.
Our choice to measure GIIPS Bank Dependence based on lead arrangers is motivated
by the central role that these banks play in originating and monitoring a syndicated loan
(Ivashina (2009)). Therefore, when a lead arranger either chooses or is forced to curtail
its lending activities, we expect this to significantly impact the borrowing firm. We follow
Ivashina (2009) and identify the lead arranger according to definitions provided by Standard
& Poor’s, which for the European loan market are stated in Standard & Poor’s Guide to the
European loan market (2010). Therefore, we classify a bank as a lead arranger if its role is
either “mandated lead arranger”, “mandated arranger”, or “bookrunner”.
The change in a firm’s financial and real variables after the start of the European
Sovereign Debt Crisis is determined by its pre-crisis lending relationships (our main vari-
able of interest), its observable and unobservable firm characteristics, and an unobserved
idiosyncratic component uncorrelated with the observable and unobservable firm character-
istics. To consistently estimate the financial and real effects for firms of having a pre-crisis
8As indicated by the term min{t, ti}. We obtain qualitatively similar results if we use the average (2005–2009) pre-crisis GIIPS Bank Dependence of each firm (see Panel C of Table A3 in the Online Appendix).The reason is that lending relationships are quite sticky (see Section III.D for more details).
14
relationship with banks affected by the sovereign debt crisis, we thus need statistical in-
dependence between a firm’s pre-crisis lending relationships, in particular, its exposure to
GIIPS banks, and the unobserved firm characteristics that affect either their financial or real
outcomes. Therefore, in our empirical analysis, we control for a rich set of firm characteristics
to remove any potential confounding factors and avoid an omitted-variable bias.
In particular, we include firm fixed effects to capture unobserved time-invariant firm
heterogeneity and firm-level control variables to capture other determinants of the firms’
corporate policies, loan demand, and loan supply. These controls include firm size, leverage,
net worth, the fraction of tangible assets, the interest coverage ratio, and the ratio of EBITDA
to total assets. For the analysis of the firms’ cash flow sensitivity of cash we also include a
firm’s cash flow and its capital expenditures.
Furthermore, GIIPS countries went through a severe recession starting in 2010 (2009 in
the case of Greece) while non-GIIPS countries were not significantly affected by economic
downturns. To alleviate concerns that our results are driven by different aggregate demand
fluctuations in our sample countries and/or in particular industries within these countries, we
add interactions between industry, year, and country fixed effects. Thereby, we remove the
possibility of spurious results due to time-varying shocks to an industry in a given country
that may have affected the credit demand of borrowing firms, as well as their real outcomes.
Perhaps our biggest challenge is the concern that a firm’s dependency on GIIPS and
non-GIIPS banks might be determined by whether this firm has business in the respective
countries. For example, a German firm might choose to borrow from a Spanish bank because
it has business in Spain. If this is the case, we could potentially overestimate the negative
real effects that can be attributed to the bank lending channel since our results could then
be driven by the possibility that a firm’s business exposure to affected countries impacted
both, its GIIPS Bank Dependence and the negative real effects.
To address this concern, and ensure orthogonality between a firm’s GIIPS Bank Depen-
dence and its unobserved characteristics, our main specification also includes foreign bank
country times year fixed effects. Consider as an example a German firm borrowing from
both a Spanish and a German bank. Besides the industry-country-year fixed effect, we in-
clude for this firm a Spain-year fixed effect to capture the firm’s potential exposure to the
macroeconomic downturn in Spain during the European Sovereign Debt Crisis.
In the following, we present descriptive statistics and explore whether our identification
15
assumptions are plausible. In Panel A of Table II, we show the pre-crisis differences of
the corporate policies across firms with a GIIPS Bank Dependence above and below the
sample median. For simplicity, we label an exposure above (below) the sample median in
the following high (low) GIIPS Bank Dependence.9 The fact that there is no systematic
difference between the real outcomes of firms with high and low GIIPS Bank Dependence
before the European Sovereign Debt Crisis indicates that the reasons for how banks and firms
match cannot explain the real outcomes for borrowing firms in a bivariate OLS context.
Panel B of Table II presents descriptive statistics for the firm-level control variables, split
into firms with high and low GIIPS Bank Dependence in the pre-crisis periods. Firms with
high GIIPS Bank Dependence tend to be larger, have more tangible assets, a higher leverage,
and lower interest coverage ratios. To test these observed differences more formally, we follow
Imbens and Wooldridge (2009) and report the normalized difference of the two subsamples,
which are defined as the averages by treatment status, scaled by the square root of the sum
of the variances, as a scale-free measure of the difference in distributions. This measure
avoids the mechanical increase in sample size, which is typically observed for t-statistics.
Imbens and Wooldridge (2009) suggest as a rule of thumb that the normalized difference
should not exceed an absolute value of one quarter. We also report standard t-statistics for
the difference in means between the two groups. As can be seen in Panel B of Table II, only
total assets is close to (but still below) this threshold (t-tests reveal significant differences for
total assets and tangibility) while all others are well below this threshold, suggesting that
firms in the two groups are comparable along most observable dimensions.
The descriptive statistics in Table II also help to rule the possibility of spurious results
due to an endogenous matching of firms and banks in the pre-crisis period that is driven by
firm quality. If low-quality firms were more likely to enter into business relationships with
GIIPS banks before the European Sovereign Debt Crisis, our results could be driven by the
fact that these firms are less resilient against the shock of the crisis. However, the fact that
there is no systematic difference between the corporate policies and real outcomes of firms
with high and low GIIPS Bank Dependence before the European Sovereign Debt Crisis and
that the correlation between GIIPS Bank Dependence and the firm control variables is in
general very low alleviates this concern. Table A2 in the Online Appendix shows that the
fraction of bank financing relative to total debt is not systematically different between firms
9Note that of course the sample median varies for the different subsamples analyzed in the paper.
16
with high and low GIIPS Bank Dependence, which alleviates the concern that firms that
have a higher dependency on GIIPS banks might be in general more bank-dependent. If
this would have been the case, these firms would be more financially constrained during a
banking crisis compared to less bank-dependent firms not because they suffer from a shock
to their banks’ health but because it is harder for them to acquire funding in general.
Furthermore, to ensure that the negative real effects for borrowing firms are actually
caused by the shock of the European Sovereign Debt Crisis on GIIPS banks, we have to rule
out two alternative explanations for how firms’ pre-crisis lending relationships could have
affected loan outcomes and, in turn, the firms’ financial and real decisions.
First, GIIPS banks might have been already less healthy than non-GIIPS banks in the
pre-crisis period. This would not have necessarily affected firms borrowing from GIIPS bank
in the pre-crisis period. However, a lower bank health might have made GIIPS banks less
resilient against the crisis. In this case, the real effects for borrowing firms would not have
been solely be due to the negative impact of the crisis on banks, but, in addition, also driven
by the fact that GIIPS banks were more vulnerable to the fallout of the crisis. To address
this possibility, Panel E of Table II presents descriptive statistics for various bank quality
measures for the pre-crisis period, split into GIIPS and non-GIIPS banks. The results show
that GIIPS banks were on average smaller and had higher equity ratios compared to non-
GIIPS banks, while impaired loans to equity and the Tier 1 ratio were not significantly
different across the two groups. Furthermore, the higher equity capitalization does not seem
to have been due to higher asset risk of GIIPS banks as the average five-year CDS spreads
were not significantly different between the two groups of banks. Therefore, we can reject
the possibility that the negative real effects for borrowing firms were caused by a lower
crisis resilience of GIIPS banks. If anything, GIIPS banks seem to have been healthier than
non-GIIPS banks before the crisis.
Second, we have to rule out the possibility that the negative real effects have been caused
by ex-ante differences in the quality of the loan syndicates. If, for some reason, healthier non-
GIIPS banks have avoided joining loan syndicates with GIIPS banks, GIIPS banks would
have been left with ex-ante worse non-GIIPS banks. For example, despite the fact that
firms with high and low GIIPS Bank Dependence did not differ significantly, there could
have been ex-ante information asymmetries between non-GIIPS banks and firms regarding
the resilience of GIIPS banks against a future crisis. Hence, in contrast to borrowing firms,
17
healthier non-GIIPS banks might have foreseen the consequences of the crisis for GIIPS
banks. This would imply that loan syndicates with GIIPS lead arrangers would have been
of lower quality to begin with, which could drive our results. To alleviate this concern, we
divide non-GIIPS banks into two groups: banks with an above and below median fraction
of deals with GIIPS banks. Comparing these two groups of banks, we find that they did not
differ in terms of capital ratios and that non-GIIPS banks that had issued a high fraction
of loans with GIIPS banks had a lower fraction of impaired loans (see Panel F of Table II).
CDS spreads again did not differ between these two groups of banks. Hence, the negative
real effects for borrowing firms do not seem to be caused by an ex-ante lower quality of
syndicates that include GIIPS banks.
B. Empirical Results for Main Specification
This section presents results for the effect of a firm’s GIIPS Bank Dependence on its
financial and real outcomes. We first divide our sample into two periods: one before the
sovereign debt crisis (2006-2008 for Greece, 2006-2009 for all other GIIPS countries) and
one during the crisis (2009-2012 for Greece, 2010-2012 for all other GIIPS countries).10 This
yields a symmetric time window around the beginning of the European Sovereign Debt Crisis.
For each bank, we construct an indicator variable, Crisisbt, which is equal to one if bank b’s
country of incorporation is in the respective crisis period at time t.
We begin by exploring the effect of the sovereign debt crisis on several firm outcomes
graphically.11. In Panels A-C in Figure 1, we plot the time series of the average employment
growth rates, the investment levels, and sales growth rates, respectively, for firms with a
high and low GIIPS Bank Dependence, as defined in Eq. (1). Figure 1 shows that, while the
pre-crisis trend was similar for the two groups of firms, a higher GIIPS Bank Dependence
led to larger negative real effects during the crisis. For example, employment growth rates
for borrowing firms with a high GIIPS Bank Dependence did not recover during the crisis
10In 2009, Greek bond yields started to diverge from the yields of other eurozone members. The Greek five-year sovereign CDS spread escalated from 100 bps in May 2009 to 250 bps by the end of the year. During2010 investors also started to lose confidence in Italy, Ireland, Portugal, and Spain. For these countries,the CDS spreads more than doubled between March and May 2010. Our results are robust to choosingalternative definitions of the crisis period, that is, setting the start of the crisis period in Greece to 2010and/or the start of the crisis period in Ireland and Portugal to 2009.
11Note that we control for observable firm characteristics such as industry, country, leverage, size, and networth in the figures.
18
period while employment rates for firms with a lower GIIPS Bank Dependence showed an
increase. Similar results can be found for the other dependent variables.
To formally investigate whether borrowing firms with significant business relationships
with GIIPS banks became financially constrained during the sovereign debt crisis, we follow
Almeida et al. (2004). They show that firms that expect to be financially constrained in
the future respond by saving more cash out of their cash flow today, whereas financially
unconstrained firms have no significant link between their cash flow and the change in cash
holdings. For the cash flow sensitivity of cash, we employ the following specification for firm
i in country j, and industry h in year t:
∆Cash ijht+1 = β1 ·GIIPS Bank Dependence ijht
+ β2 ·GIIPS Bank Dependence in Crisis ijht
+ β3 ·GIIPS Bank Dependence ijht · Cash Flow ijht
+ β4 ·GIIPS Bank Dependence in Crisis ijht · Cash Flow ijht
+ β5 · Cash Flow ijht + γ ·Xijht + Firm ijh + Industryh · Country j · Year t+1
+ ForeignBankCountryk 6=j · Year t+1 + uijht+1, (2)
where
GIIPS Bank Dep. in Crisis ijht =
∑l∈Lijhti
Γl
#Lead Arranger l
· Loan Amount l
Total Loan Amount ijhti(3)
with Γl =∑
b∈l GIIPS b · Crisisbt. Hence, Γl is the number of GIIPS lead arrangers in loan l
that already entered the crisis period. GIIPS Bank Dependence in Crisis is thus a measure
for how affected a firm is during the sovereign debt crisis due to its bank relationships. The
unit of observation is a firm-year. Our key variables of interest in the regression in Eq.
(2) is the firms’ cash flow sensitivity of cash during the crisis for firms that are dependent
on GIIPS banks (β4 in Eq. (2)). If firms with a high GIIPS Bank Dependence become
financially constrained during the sovereign debt crisis, we expect that they save more cash
out of their generated cash flows to build up a liquidity buffer against the possibility of not
being able to obtain future funding, that is, we expect β4 in Eq. (2) to be positive.
For the firms’ employment and sales growth rates, as well as their net debt, interest
19
coverage ratio, and investment levels, we estimate the following panel regressions:12
yijht+1 = β1 ·GIIPS Bank Dependence ijht
+ β2 ·GIIPS Bank Dependence in Crisis ijht
+ γ ·Xijht + Firm ijh + Industryh · Country j · Year t+1
+ ForeignBankCountryk 6=j · Year t+1 + uijht+1. (4)
The unit of observation is again a firm-year. Our key variables of interest in the regression in
Eq. (4) is the firms’ GIIPS Bank Dependence in Crisis (β2 in Eq. (4)). If firms were adversely
affected by the sovereign debt crisis through the bank lending channel, then we expect β2 in
Eq. (4) to be negative. The results of our main specification of how GIIPS Bank Dependence
is affecting firms’ financial and real decisions are presented in Table III. Column (1) provides
results for net debt ((current + non-current liabilities - cash)/total assets). The coefficient
of GIIPS Bank Dependence in Crisis (β2 in Eq. (4)) is negative, indicating that during the
sovereign debt crisis, firms with a higher exposure to GIIPS banks reduced external debt
financing more than other firms. A one standard deviation higher GIIPS Bank Dependence
during the crisis period leads to a reduction in net debt of 3.9 percentage points.13
Column (2) of Table III presents results for the degree to which firms saved cash out of
their cash flow. The coefficient of the interaction of GIIPS Bank Dependence in Crisis with
cash flow (β4 in Eq. (2)) is statistically significant at the 5% level. This positive coefficient
implies that a higher GIIPS Bank Dependence in Crisis induced firms to save more cash
out of their cash flow for precautionary reasons. Note that there is no significant relation
between the GIIPS Bank Dependence of a firm and its propensity to save cash out of its cash
flow in the pre-crisis period. More precisely, a one standard deviation higher GIIPS Bank
Dependence in Crisis implies that these firms save 3 cents more per euro of cash flow. This
compares well to the magnitudes found by Almeida et al. (2004), who show that financially
constrained firms save on average 5-6 cents per dollar of cash flow, while unconstrained
firms have no significant relation between cash flow and the change in cash holdings. Hence,
these results indicate that firms with a high GIIPS Bank Dependence became financially
12Since roughly 90% of our observations have no information on R&D expenses in Amadeus, we cannotinvestigate the impact of GIIPS bank dependence on R&D.
13Results are qualitatively similar if we use the leverage ratio instead of net debt as the dependent variable.
20
constrained during the crisis. Furthermore, Column (3) of Table III presents results for the
firms’ interest coverage ratio. Firms with a higher GIIPS Bank Dependence in Crisis had
significant lower interest coverage ratios during the crisis period, implying that, by becoming
financially constrained, they were also suffering from a deterioration in their credit quality.
The estimates suggest that a one standard deviation higher GIIPS Bank Dependence in
Crisis results in a 2.3 percentage points lower interest coverage ratio.
Acharya et al. (2014a) show that firms with higher liquidity risk are more likely to use
cash rather than bank credit lines for liquidity management because the cost of credit lines
increases with liquidity risk. This is due to the fact that banks retain the right to revoke
access to liquidity precisely in states where the firms need liquidity due to, for example, a
liquidity shortfall because of negative cash flows. Since banks themselves faced a substantial
liquidity shock during the sovereign debt crisis, we expect that firms with a high GIIPS Bank
Dependence could have lost access to their bank credit lines either because the credit lines
were not prolonged or revoked. These firms should thus increasingly have relied on cash
rather than on lines of credit to manage their liquidity.
To test this implication, we follow Acharya et al. (2014a) and hand-match our sample to
the Capital IQ database. This enables us to obtain data on the whole debt structure for some
of our sample firms including detailed information on total outstanding and undrawn credit
lines. We construct two measures for the liquidity composition of borrowing firms from these
data. First, we consider the fraction of the total amount of outstanding credit lines over the
sum of the amount of total outstanding credit line and cash. Second, we construct a measure
that captures the fraction of undrawn credit lines (i.e., the amount of a firm’s credit line that
is still available and can be drawn in case of liquidity needs) over undrawn credit lines and
cash. In Figure A2 in the Online Appendix, we plot the time series of the average total and
undrawn credit lines. We show that there was a clear change in firm liquidity management
during the sovereign debt crisis. Column (4) of Table III reports results for a firm’s overall
credit line, whereas column (5) reports results for the undrawn credit lines. Across both
specifications, we find that firms with a higher GIIPS Bank Dependence in Crisis were less
able to rely on secure funding from lines of credit.14
To summarize, our results on the firms’ financial policy indicate that firms with a high
14Due to the smaller number of observations in this analysis, we cannot use it in our sample splits inSection III.D and also cannot use foreign bank country*year fixed effects for this analysis.
21
GIIPS Bank Dependence showed the typical pattern of financially constrained firms during
the sovereign debt crisis. They relied more on cash holdings for their liquidity management
because the possibility of getting liquidity from their bank lines of credit became more
uncertain. Hence, if firms became financially constrained during the sovereign debt crisis
due to the lending behavior of their main banks, then these firms should also have responded
by adjusting their real activities.
Therefore, we next examine how the sovereign debt crisis impacted the corporate policies
of firms. We estimate panel regressions (see Eq. (4)) where yijht+1 measures employment
growth (∆log Employment), investment (CAPX /Tangible Assets), or sales growth (∆log
Sales), respectively.15 Table III presents the results. Consistent with the suggestive evidence
from Figure 1, columns (6)-(8) show that firms with a high GIIPS Bank Dependence in Crisis
had a significantly lower employment growth rate, cut investment by more, and experienced
a larger sales growth reduction than firms that were less dependent on GIIPS banks. More
precisely, a one standard deviation higher GIIPS Bank Dependence in Crisis of borrowing
firms leads to a 4.1 percentage point reduction in employment growth, a 6 percentage point
decrease in capital expenditures, and a 4.9 percentage point decrease in sales growth.
As a robustness check, we provide two alternative definitions for our key independent
variable. First, we measure a firm’s exposure to affected banks through the risk of their
“indirect sovereign debt holdings through their lenders”. More precisely, we use the weighted
average sovereign credit spread in year t, where the weights are given by firms’ “indirect
sovereign debt holdings”, that is, for each firm, we measure the exposure it has to sovereign
risk through the sovereign debt holdings of the banks from which it received loans. We then
replace the term GIIPS Bank Dependence in Crisis in Eqs. (2) and (4) with the risk of
their “indirect sovereign debt holdings”. The results are presented in Panel A of Table A3
in the Online Appendix. Second, we replace the fraction of syndicated loans provided by
GIIPS banks with the fraction of total debt that is provided by GIIPS banks in the form
of syndicated loans. This alternative definition helps us to more precisely account for the
difference in the overall bank dependence of firms. Using this alternative measure helps us
to reconfirm the validity of our earlier findings that firms with high and low GIIPS Bank
Dependence do not differ in terms of their overall dependence on banks. Panel B of Table A3
15Amadeus does not report capital expenditures. We construct a proxy for investments using the followingprocedure: (Fixed Assetst+1−Fixed Assetst+Depreciation)/(Fixed Assetst). We set CAPX to 0 if negative.
22
presents the results for this alternative way of measuring the dependence on GIIPS banks.
In both panels, all results remain economically and statistically significant.
To provide further robustness that high and low GIIPS bank dependent firms were com-
parable in terms of the outcome variables in the pre-crisis period, we conduct a placebo test
where we define the placebo crisis period as either ranging from 2006 to 2007 or from 2006
to 2008. The results are reported in Table A4 in the Online Appendix. None of the GIIPS
Bank Dependence in Placebo Crisis terms is significant for the placebo crisis definitions,
indicating that GIIPS bank dependent firms did not show significantly different trends in
the pre-sovereign debt crisis period.
C. Alternative Identification Strategy using Firms’ Business Exposure
In our main specification (see Eqs. (2) and (4)), we ensure the statistical independence
between a firm’s GIIPS Bank Dependence and its unobservable firm characteristics by con-
trolling for a firm’s business exposure to its foreign lenders’ home countries via fixed effects.
In this section, we alternatively identify the real effects caused by the decrease in loan supply
by tracking the change in corporate policies of non-GIIPS firms that had a pre-crisis relation-
ship with a GIIPS bank. The strategy is similar to the one applied by Peek and Rosengren
(1997), who also use domestic firms (in their case U.S. firms) that had borrowed from foreign
banks (in their case Japanese banks) to isolate the supply effects of the bank lending channel.
However, compared to their approach, we take two additional precautionary steps to ensure
that the results are not driven by the possibility that domestic firms that borrowed from a
foreign bank are also more likely to have business exposure to the respective country and
are thus potentially also affected by the macroeconomic downturn in this country.
First, we restrict our sample to firms that were not directly affected by the macroeconomic
shock in the periphery of the eurozone or any other part of the world. In particular, we
restrict our sample to non-GIIPS firms without subsidiaries in a GIIPS country or any
other non-EU country (e.g., a German firm without subsidiaries). To this end, we collect
information on all foreign and domestic subsidiaries of the borrowing firms in our sample,
along with information about the revenues generated by their subsidiaries.16
16Ideally, we would also like to control for the export/import dependence of our firms and their subsidiariesto specific countries. These data, however, are only available for a very small subsample of our firms inAmadeus, rendering it impossible to also control for export/import dependence.
23
To enhance our understanding of how the firm-bank relationships between non-GIIPS
firms without GIIPS subsidiaries and GIIPS banks emerged, we investigate the history of
these lending relationships prior to our sample period. Two main explanations for the
existence of these firm-bank relationship stand out, which can jointly explain roughly 90%
of the lending relationships. First, many non-GIIPS firms inherited their relationship to
a GIIPS bank through bank mergers or acquisitions (explains roughly 68% of non-GIIPS
firm - GIIPS bank links). That is, the firm had a relationship to a domestic bank that
was later acquired by a GIIPS bank. Consider as an example the German catering firm
“Die Menu Manufaktur Hofmann”, a firm located in Southern Germany that delivers food
to the cafeterias of hospitals, corporations, etc. Figure A5 in the Online Appendix shows
that its business activities are limited to Germany and Austria. Prior to our sample period,
this company obtained a loan from the Bavarian-based Bayerische Hypo- und Vereinsbank
AG, which was later acquired by the Italian bank UniCredit in 2005. After 2005, all of its
syndicated loans were originated by UniCredit. Second, the Bank of Ireland has historically
a large presence in the U.K. (explains roughly 22% of non-GIIPS firm - GIIPS bank links).
For example, in 2006 it was the fifth largest bank in terms of the number of deals in the U.K.
Therefore, a large fraction of the firm-bank relationships between non-GIIPS firms without
GIIPS subsidiaries and GIIPS banks were established due to reasons that were not related
to the geographical distribution of the firms’ business exposure.
As a second precautionary step, we thus restrict our analysis to non-GIIPS firms whose
lending relationship to a GIIPS firm can be explained by one of these two main reasons. That
is, that they either inherited their pre-crisis lending relationship with a GIIPS banks due to an
acquisition or that they borrowed from a GIIPS bank before the crisis that is very active in the
respective country’s credit market. Applying these preventive measures alleviates the concern
that a non-GIIPS firm’s dependency on a GIIPS bank might be determined by whether it has
business in the periphery of the eurozone and thus ensures statistical independence between
a firm’s GIIPS Bank Dependence and its unobservable firm characteristics.
In Panels A-C of Figure 2, we plot the time series of the average employment growth rates,
the investment levels, and sales growth rates, respectively, of the firms in this subsample.
The figure shows that also for non-GIIPS firms without GIIPS subsidiaries, firms with a
higher GIIPS Bank Dependence suffered larger negative real effects during the crisis, while
their pre-crisis trend was comparable to firms that were less dependent on GIIPS banks.
24
This finding is consistent with the evidence presented in Panels A-C in Figure 1.
For the formal analysis, we apply specifications that are very similar to our main speci-
fications from Eqs. (2) and (4). The only difference is that due to the reduced sample size,
we cannot control for both industry-country-year fixed effects and foreign bank country-year
fixed effects at the same time in the subsidiary analysis. We therefore include industry-year
and foreign bank country-year fixed effects, assuming that the industry-specific shocks in
non-GIIPS countries were similar. Panel A of Table IV provides multivariate results for the
evidence presented in Figure 2. As the table shows, all results continue to hold, confirming
that the decline in lending of banks, which were adversely affected by the sovereign debt cri-
sis, had negative real effects for borrowing firms. Panel C of Table II shows that firms in this
subsample did not differ across GIIPS Bank Dependence, which again rules out that there
was an endogenous matching of firms and banks in the pre-crisis period that was driven by
firm quality. As a further robustness check, we restrict the sample to firm-bank relationships
where firms inherited their banks through M&A transactions. That is, we only consider cases
where non-GIIPS firms borrowed from a domestic bank that was later acquired by a GIIPS
bank. This yields qualitatively similar results (see Panel B of Table IV). To further rule out
that our results are driven by firms that are incorporated in export dependent industries,
Panel C of Table IV restricts the analysis to non-GIIPS firms without subsidiaries in GIIPS
or any other non-EU country that are operating in non-tradable sectors. We follow Mian
and Sufi (2014) to identify tradable and non-tradable sectors. All results continue to hold
for firms operating in non-tradable sectors.
D. Supply and Demand Factors of Bank Lending
If the real effects documented in Section III.C were actually caused by a reduction in loan
supply from banks affected by the European Sovereign Debt Crisis, we would expect that
the negative real effects from having a high GIIPS Bank Dependence were less pronounced
for firms that were less prone to becoming financially constrained. In particular, we should
observe smaller or no significant real effects of having a business relationship with a bank
affected by the crisis (i) for firms that, relative to the decrease in loan supply, experienced an
even larger decrease in loan demand and (ii) for firms that were very likely able to substitute
the reduction in loan supply with other means of financing. Therefore, to assure that our
25
results are indeed driven by a loan supply decrease, in this section, we compare the negative
real effects incurred by these different subsets of firms.
We start with testing whether firms that had a relative low demand for bank loans during
the sovereign debt crisis suffered less real effects through the bank lending channel compared
to firms that had a high demand for loans. In particular, firms that were heavily exposed
to the negative macroeconomic shock in the periphery of the eurozone had presumably a
very low or no demand for additional bank loans as a firm’s demand for bank financing
is strongly influenced by its investment and growth opportunities. For these firms, the
reduction in loan supply due to a business relationship with a bank affected by the crisis
should be overcompensated by the loan demand decrease and thus should be without effect.
As a result, while of course having experienced very significant real effects due to the negative
macroeconomic shock, these firms should not have suffered additional negative real effects
from encountering a drop in loan supply.
To check this, we use the revenue information for all foreign and domestic subsidiaries of
the firms in our sample to determine each firm’s geographical revenue distribution. As shown
by the results in Panel A-C of Table IV, non-GIIPS firms that had no observable business
exposure to GIIPS countries experienced strong negative real effects of having a high GIIPS
Bank Dependence. In contrast, according to the above-mentioned arguments, we would
expect that the real effects of having a high GIIPS Bank Dependence are significantly less
pronounced for non-GIIPS firms with business exposure to GIIPS countries (e.g., a German
firm with subsidiaries in Italy or Spain), since these firms should have had a significant lower
demand for bank loans. To test this prediction, we rerun the regressions applied in Section
III.C, that is, we control for industry*year and foreign bank country*year fixed effects to
absorb possible unobserved macroeconomic shocks. Indeed, looking at Panel D of Table IV,
these firms seem less financially constrained when having a high GIIPS Bank Dependence in
Crisis and, in line with this result, we find weaker negative effects for employment and no
negative effects for investments and sales growth.
As an additional robustness check, we do the same exercise for GIIPS firms and divide the
firms according to their business exposure to non-GIIPS countries. GIIPS firms that were
less exposed to the crisis because they have a large fraction (highest tercile) of their revenue
generated by subsidiaries in non-GIIPS countries (e.g., a Spanish firm that has a significant
fraction of its revenues generated by a German subsidiary) should have had a higher demand
26
for loans compared to GIIPS firms that generate their revenue mainly in GIIPS countries.
Hence, we expect to see larger negative real effects as a result of having a high GIIPS Bank
Dependence for the former group of firms compared to the latter group. Panel A of Table
A5 in the Online Appendix shows that GIIPS firms with a high fraction of their revenue
generated by foreign non-GIIPS subsidiaries experienced significant real effects as a result
of having a pre-crisis lending relationship with GIIPS banks.17 As expected, when looking
at Panel B of Table A5, we find weaker effects both in terms of economic and statistical
significance for GIIPS firms with a majority of their business in GIIPS countries (e.g., a
Spanish firm without subsidiaries).
Next, along the same lines, we investigate whether firms that were more likely able to
substitute a possible reduction in loan supply with other means of financing suffered less
real effects from having a high GIIPS Bank Dependence in Crisis than firms that are more
bank-dependent. In particular, we split our sample into listed and non-listed firms, as well
as rated and unrated firms. The underlying assumption is that non-listed and unrated
firms have fewer alternative funding sources, since they are less likely to be able to raise
additional public equity or issue bonds, implying that these firms are more bank-dependent
(Sufi (2007)). Moreover, there is less publicly available information for these firms, requiring
more monitoring and information collection by banks. Overall, in case bank-related loan
supply factors played a role during the crisis, non-listed and unrated firms should thus have
been much more affected when having a high dependency on GIIPS banks than listed and
rated firms, which have access to alternative sources of funding.
Panel A of Table V presents the results for the subsample of listed firms, while Panel B
presents the results for non-listed firms. The table shows that our results continue to hold for
non-listed firms; however, we do not find any evidence that listed firms showed the typical
behavior of a financially constrained firm or that they had significantly negative real effects
during the crisis period. The results for the sample split between rated and unrated firms
are shown in Panels C and D, respectively. All our results are driven by firms without access
to the public bond market. Only for unrated firms do we find significant real effects that can
be attributed to banks’ lending behavior. Therefore, in line with the findings of Becker and
Ivashina (2014b), firms with alternative funding sources thus seem to be able to substitute
17Panel D of Table II shows that the firms in this subsample did not differ across GIIPS Bank Dependencein the pre-crisis period, which again rules out a firm quality driven endogenous bank-firm matching.
27
the lack of bank financing, whereas non-listed and unrated firms cannot easily alter their
funding sources and thus suffered significant real effects when having a high dependency on
banks affected by the sovereign debt crisis. Panel E and F of Table V, in which we report
results separately for firms that did issue bonds during the sample period and firms that did
not, reconfirm this finding. In line with the previous results, only firms that were not able
to substitute the lack of bank lending by issuing bonds experienced real effects of having
relationships to banks affected by the sovereign debt crisis.
Besides being better able to substitute a loan supply decrease with funds from other
financing sources, larger and public firms should also find it easier than smaller and private
firms to borrow from sources other than their previous lender. Therefore, we investigate in
the following in greater detail the evolution of bank relationships during the crisis period and
test whether the real effects of having a high GIIPS Bank Dependence were more pronounced
for firms that were not able to acquire a new bank relationship during the crisis.
Previous work (e.g., Chodorow-Reich (2014)) documents that bank relationships in the
syndicated loan market are sticky, suggesting that most firms do not switch banks. Indeed,
we find in our sample as well that for roughly 75% of firms the GIIPS Bank Dependence
does not change throughout the sample period. As expected, mostly listed firms are able
to switch banks since for these firms more publicly information is available, which reduces
asymmetric information problems. In contrast, roughly 70% of firms with constant bank
relationships are non-listed firms, as shown in Panel C of Table VI. Panel A of Table VI
shows that all results continue to hold for the subsample of firms that do not switch banks,
whereas we do not find significant effects for firms that switch banks, as shown in Panel B.
These results again confirm that the limited access to funding due to lending relationships
with banks affected by the European Sovereign Debt Crisis played a major role in causing the
negative real effects experienced by the affected borrowing firms. Therefore, two important
contributions of this study are the documentation of (i) strong spillovers from high-spread
euro area sovereigns to the local real economy through the bank lending channel and (ii)
significant cross-border spillovers from the crisis in GIIPS countries to firms in non-GIIPS
countries are transmitted through the bank lending channel. Therefore, while the euro
greatly benefits its members by deepening the degree of financial integration, the extensive
cross-border bank lending has also facilitated the transmission of shocks across the eurozone.
28
E. Aggregate Effects
With some additional assumptions, we can use the firm-level results for the different
subsamples from Tables IV and A5 (which is in the Online Appendix) to inform the debate
regarding the aggregate effects of the loan supply shock of the European Sovereign Debt
Crisis. The strategy to estimate the aggregate effects is similar in spirit to the procedure
used in Chodorow-Reich (2014). For each borrower, we estimate what his performance
would have been if he had borrowed from the least affected syndicate, which in our case
is a syndicate without GIIPS banks in the lead arranger position. We employ a partial
equilibrium analysis, that is, we assume that the overall real effect equals the sum of the
real effects at the firm level. Moreover, we assume that the least affected loan syndicate did
not shift its lending supply function during the crisis. We explain our strategy to estimate
aggregate effects using employment growth rates as an example. We perform the same
analysis for investment and sales growth rates. We start by defining the counterfactual
employment growth rate of firmijh if it had borrowed entirely from non-GIIPS banks:
yijht = yijht − β1 ·GIIPS Bank Dep.ijht − β2 ·GIIPS Bank Dep. in Crisis ijht, (5)
where yijht denotes the fitted value from the respective regression. In the case of employment,
we then use the counterfactual employment growth rate to calculate the counterfactual
employment level Empijht and similarly the fitted value employment level Empijht. The
total losses due to the bank lending shock during the crisis period are then given by
Total Losses =∑
t∈[ti+1,T ]
∑ijh
[Empijht − Empijht], (6)
where T is the last sample year (i.e., 2012). The fraction of the sample net employment
change during the crisis that is caused by banks’ lending behavior is then given by
∑t∈[ti+1,T ]
∑ijh [Empijht − Empijht]∑
ijh[EmpijhT − Empijhti ]. (7)
In the following, we focus on two subsamples of firms, where we are best able to disentangle
the macroeconomic shock from the bank lending shock. Looking at the results for non-
29
GIIPS firms without subsidiaries in GIIPS or other non-EU countries, we find that overall
employment decreased by 1.6% during the European Sovereign Debt Crisis. Our effect
accounts for 25% of this decline, that is, firms would have cut employment by 25% less,
had they borrowed from loan syndicates without GIIPS banks acting as lead arrangers.
Similarly, firm investment fell by 2%; 24.8% of this decline in investment can be explained
by the contraction in bank lending. Finally, sales decreased by 2%; 21.4% of this reduction
in sales can be attributed to the loan supply shock.
Considering the sample of GIIPS firms with a high fraction of revenue generated by
non-GIIPS subsidiaries, we find that overall employment fell by 5.6% during the European
Sovereign Debt Crisis period. We can attribute 53.6% of this decline to the bank lending
supply shock. Similarly, investment fell by 13%, of which 43.2% can be explained by banks’
lending behavior. For the evolution of sales, we find an overall decrease of 3.6% over the
European Sovereign Debt Crisis period, of which we can explain 37% of the decline.
There are two things to note about these magnitudes. First, perhaps not surprisingly,
the reduction in employment, investment, and sales was smaller in non-GIIPS countries,
which were less affected by the European Sovereign Debt Crisis, than in GIIPS countries.
Second, we can explain less of the overall reduction in employment, investment, and sales in
non-GIIPS countries. The main reason for this is that a considerable number of non-GIIPS
firms without subsidiaries in GIIPS or other non-EU countries have zero exposure to GIIPS
banks, implying that for them yijht equals yijht. Put differently, for a substantial number of
non-GIIPS firms in this subsample, there are no loan supply disruptions caused by GIIPS
banks, implying that, overall, we can explain less of the overall macroeconomic evolution.
IV. Active and Passive Transmission Channels
Given that firms that had a pre-crisis lending relationship with a bank affected by the
European Sovereign Debt Crisis suffered significant negative real effects as a result, in this
section, we shed more light on how exactly sovereign credit risk translated into the bank
lending contraction and the resulting negative real effects for firms. Compared to financial
crises, which only impair the banks’ financial health, the impact of sovereign crises on bank
lending is much more complex. There are at least three potential channels through which
banks might have been affected by the sovereign debt crisis: one passive and two active.
30
The passive channel works through the dramatic increase in risk of GIIPS sovereign debt
during the crisis. EBA data show that banks generally had large direct holdings of domestic
government debt. Therefore, the increase in risk of GIIPS sovereign debt directly translated
into losses that weakened the asset side of GIIPS banks’ balance sheets and as a result made
these banks riskier (Acharya and Steffen (2015)). This can lead to losses for the banks via
three channels: (i) banks sell government bonds realizing a loss; (ii) bonds are in the trading
book and therefore marked to market; and (iii) bonds are pledged to the European Central
Bank (ECB), which makes margin calls in case the value of the collateral falls. Table A6 in
the Online Appendix shows that indeed there was a significant positive relationship between
banks’ GIIPS sovereign debt holdings and their CDS spreads over the crisis period. To cope
with these losses, GIIPS banks might have deleveraged and reduced lending to the private
sector (Bocola (2014) explores this mechanism in a theoretical model). We call this the “hit
on balance sheet channel”. This effect is amplified by the significant withdrawal of wholesale
funding by U.S. money market funds (Ivashina et al. (2015)).
To get a better idea of how strongly a bank was affected by the risk of its domestic
sovereign portfolio, we construct a similar measure as in Popov and Van Horen (2014), and
measure the exposure to domestic sovereign risk of bank b in year t as follows:
Domestic Sovereign Debt Risk bt =Domestic Sov. Bondholdingsbt · CDS t
Total Assetsbt
. (8)
Given that the sovereign bondholdings are multiplied with the respective CDS spreads of
the bonds, this measure accounts for the amount of bondholdings of the respective bank,
as well as for the risk associated with these holdings. We classify a bank as affected if its
CDS-weighted holdings of domestic sovereign debt are above the sample median.
The two active channels are the risk-shifting channel and the moral suasion channel.
The risk-shifting motive arises since, as the default risk of GIIPS countries increased, highly
levered GIIPS banks had an incentive to increase their domestic sovereign bondholdings
(Diamond and Rajan (2011); Crosignani (2014)). The reason for this behavior is as follows.
In case a bank wants to engage in risk-shifting, it is looking for an asset that is correlated with
its other sources of revenue and that, at the same time, offers a comparatively high expected
return. In particular, the asset should only generate losses in states of the world in which the
bank is in default anyway. Since banks usually have large holdings of domestic government
31
debt (e.g., the holdings of domestic sovereign bonds of Unicredit and Intesa in mid-2011
amounted to 121% and 175% of their core capital, respectively18), they would fail anyway
as soon as their domestic government is not able to repay its sovereign debt. Furthermore,
during the European Sovereign Debt Crisis, the sovereign debt of GIIPS countries promised
a high return, thereby making this asset class very attractive for risk-shifting purposes. In
addition, according to the “Capital Requirements Directive” (CRD), European regulators
consider that sovereign bonds are risk-free (i.e., attach zero risk weights); thus, banks do
not need to hold any capital against potential losses on government bonds. On top of that,
European regulators removed the concentration limits for sovereign debt exposures, while
a bank’s exposure to a single borrowing firm is limited to 25% of its Tier 1 capital. For
these reasons, sovereign debt allows larger bets compared to other asset classes, in particular
corporate loans. Furthermore, for risk-shifting purposes, corporate loans have in addition
the disadvantage that they have an idiosyncratic risk component, while the banks’ domestic
sovereign debt holdings all default in the same states of the world.
One might argue that, for risk-shifting purposes, banks might have had an incentive to
buy the GIIPS sovereign debt that generates the highest yields, which during the European
Sovereign Debt Crisis was Greek sovereign debt. However, even though there probably is a
positive correlation between the default probability of Greek and other GIIPS sovereign debt,
the relationship is far from being perfectly correlated. Since non-Greek GIIPS banks hardly
had any exposure to Greek sovereign debt during the European Sovereign Debt Crisis19, it
is very unlikely that non-Greek GIIPS banks would fail if Greece defaults on its sovereign
debt. Therefore, for these banks, domestic sovereign debt dominates Greek sovereign debt
with regard to its suitability as a risk-shifting asset. Due to liquidity, leverage, and capital
constraints induced by market forces and regulatory constraints, this incentive of GIIPS
banks’ to engage in risk-shifting by loading up on risky domestic sovereign debt might have
led to a crowding-out of lending to the private sector during the sovereign debt crisis.
We apply two different measures to identify which banks are weakly-capitalized and
thus more prone to risk-shifting behavior. First, we consider a GIIPS bank to be weakly-
capitalized if its total equity to total assets ratio (obtained from SNL Financial) at the end
18“Europe’s Banks Struggle With Weak Bonds” by Landon Thomas Jr., NYTimes.com, August 3, 2011.19In fact, at the beginning of the crisis in early 2010 periphery banks had 90% of their GIIPS sovereign
bond holdings from their own sovereign; this number rose to 97% by the end of 2012 (see Crosignani (2014)).
32
of 2009 is below the sample median. Second, as a robustness check, we use the banks’ rating
before the sovereign debt crisis (i.e., at the end of 2009) as an alternative measure of bank
health. To determine the rating cutoff, we follow Drechsler et al. (2014) and use the ratings
(obtained from Bloomberg) from the main rating agencies (Moody’s, Standard & Poor’s,
and Fitch). We then assign a numerical value to each rating: 1 for AAA, 2 for AA+, and so
on, and compute the median rating for each bank. This measure has the advantage that it
is based on assessments by market participants, rather than on accounting measures.
The second active channel that might have led to a crowding-out of corporate lending is
the moral suasion channel (see Becker and Ivashina (2014b)). As the sovereign debt crisis
peaked, governments in GIIPS countries faced severe problems in refinancing their debt.
In these cases, governments may have turned to their domestic banks and forced them to
purchase domestic sovereign debt.
We use three proxies to measure the degree to which banks are prone to the moral suasion
of their sovereigns. First, following Acharya and Steffen (2015), we use data about govern-
ment interventions compiled from information disclosed on the official EU state-aid websites
to classify banks into intervened and non-intervened banks.20 The idea is that intervened
banks are more prone to moral suasion as the influence of governments on these banks is
arguably larger. We classify a bank as affected if it received some form of financial aid from
its government. Second, we follow Iannotta et al. (2013) and compile government bank own-
ership data from Bankscope. As shown in De Marco and Macchiavelli (2014), government
ownership seems to have an influence on banks’ domestic sovereign bondholdings as banks
with a high government ownership share hold, in general, significant more domestic sovereign
debt compared to other banks. We construct an indicator variable “High Fraction of Gov-
ernment Ownership”, which is equal to one if the share owned by the government for a given
bank in a certain year is above the median of the distribution. Lastly, government control
over banks can also be measured by government board representation. We follow Becker and
Ivashina (2014b) and extract the fraction of directors affiliated with the government from
the BoardEx database. For our empirical analysis, we construct an indicator variable equal
to one if the fraction of government affiliated directors exceeds the median.
20The data can be obtained from: http://ec.europa.eu/competition/elojade/isef/index.cfm?
clear=1&policy_area_id=3.
33
A. Change in Banks’ Sovereign Holdings
Both active channels, the risk-shifting and the moral suasion channel, are consistent
with an increase in domestic sovereign bondholdings over the crisis period, which makes
their disentanglement particularly challenging. Therefore, we start with exploring whether
and which banks changed their sovereign debt holdings after the outbreak of the European
Sovereign Debt Crisis.
In Figure 3, we plot the evolution of GIIPS (Panel A) and domestic (Panel B) sovereign
debt exposure over time for banks incorporated in non-GIIPS countries (left part of graph)
and GIIPS countries (right part of graph). The blue solid line shows the evolution of the sum
of the respective banks’ sovereign bondholdings scaled by the sum of banks’ total assets at
the end of the respective year. The red dashed line shows the sum of sovereign bondholdings
multiplied by the sovereign’s CDS spread as a fraction of the sum of total assets.
Figure 3 shows that most of the GIIPS sovereign bondholdings held by GIIPS banks
are domestic, implying a very high correlation between measures of bank affectedness based
on overall GIIPS sovereign bondholdings and domestic sovereign bondholdings. In addi-
tion, Figure 3 indicates that the riskiness of GIIPS sovereign bondholdings spiked in the
crisis period, which severely affected the health of GIIPS banks due to their large domes-
tic sovereign bondholdings, as shown by the significant positive relationship between banks’
GIIPS sovereign debt holdings and their CDS spreads (see Table A6 in the Online Appendix).
Furthermore, Panel A of Figure 3 shows that, while non-GIIPS banks slightly decreased
their GIIPS sovereign debt exposure between 2009 and 2011, GIIPS banks kept their GIIPS
sovereign debt holdings constant. Regarding the domestic sovereign debt holdings, Panel B
of Figure 3 documents that both GIIPS and non-GIIPS banks hold their domestic sovereign
exposure constant in our sample period. Hence, this preliminary evidence is not consistent
with the risk-shifting and moral suasion hypotheses.
However, even though GIIPS banks on average did not significantly increase their do-
mestic sovereign bondholdings, as shown in Panel B of Figure 3, we cannot rule out that
the risk-shifting channel and the moral suasion channel played an important role for banks’
lending behavior and the resulting real effects of borrowing firms. The fact that, on aver-
age, the domestic sovereign bondholdings of GIIPS banks do not change is also consistent
with distressed banks (i.e., those with high risk-shifting incentives) increasing their holdings,
while other banks decrease their domestic sovereign bondholdings. Similarly, only those
34
GIIPS banks that are very dependent on their governments might be pressured to increase
their domestic sovereign bondholdings, while less dependent banks might not. To investigate
these possibilities, we analyze the respective subsets of GIIPS banks separately.
We start with the risk-shifting channel and plot the evolution of the domestic sovereign
debt exposure over time separately for well-capitalized (low leverage) and weakly-capitalized
(high leverage) GIIPS banks. As can be seen from Panel A of Figure 4, weakly-capitalized
GIIPS banks increased their holdings of domestic sovereign debt significantly by roughly 4
percentage points of total assets. This indicates that risk-shifting might have played a role
for the cutback in lending of highly leveraged banks. To test the robustness of this finding,
we use the banks’ rating before the sovereign debt crisis (i.e., at the end of 2009) as an
alternative measure of bank health. In Panel B of Figure 4, we plot the evolution of the
domestic sovereign debt exposure for high-rated GIIPS banks (left part of graph) and low-
rated GIIPS banks (right part of graph), where we consider a GIIPS bank to be low-rated
if its median rating is below the A+ threshold. Results remain qualitatively unchanged,
which again supports the risk-shifting hypothesis. To test whether this increase in domestic
sovereign debt holdings of banks prone to risk-shifting is also statistically significant, Table
A7 in the Online Appendix presents regression results where the dependent variable is the
change in a bank’s domestic sovereign debt holdings over the 2009 to 2011 period. As
can be seen from Panels A and B, indeed both high leverage and low rating GIIPS banks
significantly increased their holdings of domestic sovereign debt during the crisis.
Next, we analyze whether GIIPS banks increased their domestic sovereign bondholdings
during the sovereign debt crisis due to pressure from their governments. Panels C–E of Table
A7 in the Online Appendix show that for none of the moral suasion proxies (i.e., government
intervention, government bank ownership, or government control) there are significant effects
on the change in a bank’s domestic sovereign debt holdings.
B. Lending
Given that risk-shifting seems to have played an important role for the increase in do-
mestic sovereign debt holdings, while we do not find evidence for moral suasion, we now
investigate the importance of the three potential channels, that is, hit on balance sheet,
risk-shifting, and moral suasion, for the lending supply contraction formally.
35
B.1. Methodology
To test the importance of the different channels for the reduction in bank lending, we
apply a modified version of the Khwaja and Mian (2008) estimator, which exploits multiple
bank-firm relationships before and during the sovereign debt crisis to control for loan demand
and other observed and unobserved borrowing firm characteristics.
While we observe a large number of firms borrowing from multiple banks, we face some
constraints in data availability, that render it unfeasible to use the original setup of Khwaja
and Mian (2008). First, in contrast to their approach, our dataset contains information only
at the time of the origination of the loan, which does not allow us to observe changes over
time for a particular loan (e.g., on credit line drawdowns). Second, the syndicated loans in
our sample generally have long maturities. Taken together, these two facts imply that a large
number of observations in our sample experience no significant year-to-year change in bank-
firm lending relationships. This requires us to modify the Khwaja and Mian (2008) estimator
and aggregate firms into clusters to generate enough time series bank lending heterogeneity
to meaningfully apply the estimator to our data. In particular, we track the evolution of the
lending volume and loan spreads from a specific bank to a certain firm cluster.
To this end, we form firm clusters based on the following three criteria, which capture
important drivers of loan demand, as well as the quality of firms in our sample: (1) the
country of incorporation; (2) the industry; and (3) the firm rating. The main reason for
aggregating firms based on the first two criteria is that firms in a particular industry in a
particular country probably share a lot of firm characteristics and were thus likely affected
in a similar way by macroeconomic developments during our sample period. Our motivation
behind forming clusters based on credit quality follows from theoretical research in which
credit quality is an important source of variation driving a firm’s loan demand (e.g., Diamond
(1991)). To aggregate firms into clusters, we assign ratings estimated from interest coverage
ratio medians for firms by rating category provided by Standard & Poor’s.21 This approach
exploits the fact that our measure of credit quality, which is based on accounting information,
is monotone across credit categories (Standard&Poor’s (2006)). We follow Standard & Poor’s
and assign ratings on the basis of the three-year median interest coverage ratio of each firm,
where the median is calculated from the period preceding the sovereign debt crisis.
21Note that only a small fraction of all firms in our sample have a rating from one of the rating agencies.
36
We use the following panel regression to estimate the annual change in loan volume
provided by bank b in country j to firm cluster m in year t:
∆V olumebjmt+1 = β1 ·GIIPS Bank bj · Crisisbt+ β2 · Affected GIIPS Bank bj · Crisisbt+ γ ·Xbjt + Firm Clusterm · Year t+1
+ Firm Clusterm · Bank bj + ubjmt+1. (9)
The unit of observation is a bank-year-firm cluster. Besides controlling for bank character-
istics (log of total assets, capital ratio, ratio of impaired loans to equity) we add firm-cluster
times year fixed effects. This allows us to control for any observed and unobserved charac-
teristics that are shared by firms in the same cluster and that might influence loan outcomes.
Moreover, we interact firm-cluster and bank fixed effects. Thereby, we exploit the variation
within the same firm cluster and bank over time. This not only controls for any unobserved
characteristics that are shared by firms in the same cluster, or bank heterogeneity, but also
for relationships between firms in a given cluster and the respective bank.
B.2. Results
Panel A of Table VII presents the results for the change in lending volume. The dependent
variable represents the annual change in loan volume provided by a given bank to a given
firm cluster. To examine whether the results for the financial and real effects of borrowing
firms from Section III are indeed associated with a reduction in bank lending, we start with
our broad measure for a bank’s affectedness (i.e., the banks’ country of incorporation) used
to capture all three potential channels. Column (1) of Panel A in Table VII presents the
results for this proxy. The coefficient of the GIIPS bank dummy during the crisis period is
negative and statistically significant, which is consistent with the interpretation that GIIPS
banks significantly decreased their lending volume to the real sector during the sovereign
debt crisis. This finding thus supports the results presented in Section III that the lending
contraction of banks affected by the crisis was an important driver for the negative real
effects experienced by their borrowing firms.
In the following, we present the results for the three potential channels that may affect
the lending behavior of banks to the real sector. Column (2) in Panel A of Table VII shows
37
the results for the hit on balance sheet channel. The coefficient of the sovereign risk dummy
variable interacted with the crisis dummy variable is negative and significant. This finding
indicates that banks with larger sovereign risk in their portfolios reduced lending during
the crisis by a larger fraction than banks with lower sovereign risk exposure.22 Therefore,
the risk associated with the sovereign bondholdings and thus the losses incurred due to the
sovereign debt crisis indeed play an important role for the lending behavior of banks.
Next, we test whether the reduction in bank lending is also driven by risk-shifting in-
centives, that is, whether weakly-capitalized GIIPS banks, which increased their domestic
sovereign bondholdings during the sovereign debt crisis, also decreased their corporate lend-
ing. The results are presented in columns (3) and (4) in Panel A of Table VII. We find that
weakly-capitalized GIIPS banks cut their lending to the real sector more than well-capitalized
GIIPS banks, irrespective of how we proxy for risk-shifting incentives. These results indicate
that the active increase in domestic sovereign bondholdings, shown in Figures 4, results in a
crowding-out of private sector lending by weakly-capitalized GIIPS banks.
Finally, we examine whether the moral suasion channel affects bank lending during the
sovereign debt crisis. Columns (5)–(7) of Table VII present the results for our three proxies
for moral suasion: government interventions, government ownership, and government control
over banks. The point estimates of the three proxies for moral suasion interacted with the
crisis indicator variable are not significantly different from zero. For example, the interaction
of the intervened GIIPS bank variable with the crisis indicator variable is zero in magnitude
and not statistically significant. Overall, we do not find evidence that moral suasion has
played a role in the banks’ lending decisions in our sample period.
Panel B of Table VII shows the robustness of our results when we use the change in
the spread of newly issued loans instead of the change in volume as the dependent variable.
We find qualitatively similar results here. Taken together, our evidence indicates that the
balance sheet hit caused by the increase in sovereign risk and the risk-shifting channel are
of first-order importance regarding the effect of the sovereign debt crisis on bank lending
behavior. However, we note that GIIPS banks might have engaged in even greater risk-
shifting and/or might have been forced by their governments to buy domestic debt after
22As described above, for most banks the majority of their sovereign bondholdings are domestic, whichis why the coefficients for the domestic and GIIPS sovereign risk exposure measures are very similar inmagnitude. For brevity, we only report the results for the domestic sovereign risk exposure measures.
38
the end of our sample period, that is, after 2012. Furthermore, GIIPS governments might
have implicitly encouraged banks to engage in risk-shifting by implementing regulations that
favor such behavior. Alternatively, governments might not have faced the need to pressure
banks into buying more domestic sovereign debt since the weakly-capitalized banks did so
anyway. Finally, government moral suasion could have been stronger for less healthy banks
as they were likely to need government assistance in the future.
C. Financial and Real Outcomes
C.1. Methodology
We now examine which of the three channels contributed to the financial and real effects of
borrowing firms. We apply regressions similar to the ones from Eqs. (2) and (4). In addition,
we construct several variables denoted Affected Bank Dep. at the firm-year level, reflecting
how much credit comes from affected banks in a given year, where we distinguish between
affected and non-affected banks using the same proxies as in Table VII. The computation is
similar to Eq. (1), with the only difference being that Φl =∑
b∈l Affected b, where Affected b
is a dummy variable that indicates whether lead arranger bank b is affected through the
respective channel, in which case it is equal to one and otherwise it is zero. For the firms’
employment and sales growth rates, as well as their net debt, interest coverage ratio, and
investment levels, we estimate the following panel regressions:
yijht+1 = β1 ·GIIPS Bank Dependence ijht
+ β2 · Affected Bank Dependence ijht
+ β3 · Affected GIIPS Bank Dependence ijht
+ β4 ·GIIPS Bank Dependence in Crisis ijht
+ β5 · Affected GIIPS Bank Dependence in Crisis ijht
+ γ ·Xijht + Firm ijh + Industryh · Country j · Year t+1
+ ForeignBankCountryk 6=j · Year t+1 + uijht+1. (10)
Affected GIIPS Bank Dependence is also computed similar to Eq. (1), with the only difference
being that now Φl =∑
b∈l Affected b · GIIPS b, whereas Affected GIIPS Bank Dependence
39
in Crisis is calculated similar to Eq. (3), with the only difference being that now Γl =∑b∈l Affected b · GIIPS b · Crisisbt. The unit of observation is again a firm-year. Our key
variable of interest in regression Eq. (10) is the firms’ dependence on affected GIIPS banks
during the crisis (β5 in Eq. (10)). If affected GIIPS banks reduced their loan supply during
the crisis, we expect that firms with lending relationships to these banks should incur negative
real effects, that is, we expect β5 in Eq. (10) to be negative. Along the same lines, we modify
the regression from Eq. (2) to analyze the change in the cash flow sensitivity of cash during
the crisis.
C.2. Results
We begin by reporting results for the passive bank lending channel, that is, whether the
increase in sovereign risk that induced banks to deleverage and thus decrease their corporate
lending, affected borrowing firms by making them financially constrained. The results are
presented in Table VIII. In Panel A, the affected indicator variable is equal to one if a bank’s
GIIPS sovereign portfolio credit risk exposure is above the sample median. In Panel B, the
domestic sovereign portfolio credit risk exposure is used to distinguish between affected and
non-affected banks. The coefficient of the variable for being dependent on an affected bank
in the crisis period is negative and significant for all dependent variables. Therefore, Panels
A and B show that the hit on the affected banks’ balance sheets resulted in negative financial
and real effects for firms that have a lending relationship with these banks.
Next, we examine whether the active bank lending channels, that is, the risk-shifting and
the moral suasion channel, led to real effects for borrowing firms. Table IX reports results for
the risk-shifting channel. The affected bank measure is based on the GIIPS banks’ leverage
(Panel A) or rating (Panel B), respectively. The results for both bank health proxies indicate
that the real effects were much stronger for firms that have a lending relationship with a
GIIPS bank that was weakly-capitalized and thus not able to cope with losses due to the
sovereign debt crisis. These banks engaged in risk-shifting by increasing their risky domestic
sovereign bondholdings. This behavior decreased bank lending even more compared to well-
capitalized GIIPS banks that were better able to manage their losses incurred during the
sovereign debt crisis and thus had less or no risk-shifting incentives.
Finally, the results for the moral suasion proxies are presented in Table X. We find that
our moral suasion proxies do not appear to have an effect on the corporate policies of bor-
40
rowing firms. Neither government interventions, nor government board seats or government
ownership have any explanatory power in the cross-section. This is consistent with the
fact that there is now statistical significant relationship between our moral suasion proxies
and the lending behavior of banks in our sample. Therefore, banks’ exposures to impaired
sovereign debt and risk-shifting behavior of undercapitalized banks seem to be of first-order
importance for explaining the negative real effects suffered by European firms, while we find
no evidence that moral suasion by governments to buy more domestic sovereign debt has
played a major role.
V. Conclusion
In this paper, we show that the European Sovereign Debt Crisis and the resulting credit
crunch in the eurozone periphery caused significant negative real effects for borrowing firms
in Europe. We find that firms that had a pre-crisis lending relationship with banks that
suffered from the sovereign debt crisis became financially constrained during the crisis. As a
result, these firms had on average lower employment growth rates, lower levels of investment,
and lower sales growth rates. This holds true for both GIIPS and non-GIIPS firms.
Moreover, we shed light on the question of how the European Sovereign Debt Crisis
actually induced a contraction in bank lending and the resulting real effects for borrowing
firms. We document that the negative real effects that can be attributed to the bank lending
channel are primarily associated with (i) banks from GIIPS countries facing losses on their
substantial domestic sovereign bondholdings, and (ii) the resulting incentives of undercapi-
talized banks from GIIPS countries to engage in risk-shifting behavior by buying even more
domestic sovereign bonds, thereby crowding out corporate lending.
Therefore, our findings foster the understanding of the unfolding of the European Sovereign
Debt Crisis and yield important insights on how to overcome the economic recession in the
periphery of the eurozone. Our results indicate that an effective bank recapitalization could
significantly contribute to the economic recovery in Europe, since the pressure to delever-
age due to the banks’ weakened financial health and the resulting risk-shifting incentives of
undercapitalized banks seem to be the most important determinants for the stagnation of
bank lending in Europe and, in turn, the firms’ low investment levels.
41
REFERENCES
Acharya, Viral V, Heitor Almeida, Filippo Ippolito, and Ander Perez, 2014a, Credit lines as
monitored liquidity insurance: Theory and evidence, Journal of Financial Economics 112,
287–319.
Acharya, Viral V, Itamar Drechsler, and Philipp Schnabl, 2014b, A pyrrhic victory? bank
bailouts and sovereign credit risk, Journal of Finance 69, 2689–2739.
Acharya, Viral V, and Sascha Steffen, 2015, The greatest carry trade ever? understanding
eurozone bank risks, Journal of Financial Economics 115, 215–236.
Adrian, Tobias, Colla Paolo, and Hyun Song Shin, 2013, Which financial frictions? parsing
the evidence from the financial crisis of 2007-09, in Daron Acemoglu, Jonathan Parker,
and Michael Woodford, eds., NBER Macroeconomics Annual 2012 (Chicago: University
of Chicago Press, 2013).
Almeida, Heitor, Murillo Campello, and Michael S Weisbach, 2004, The cash flow sensitivity
of cash, Journal of Finance 59, 1777–1804.
Balduzzi, Pierluigi, Emanuele Brancati, and Fabio Schiantarelli, 2014, Financial markets,
banks’ cost of funding, and firms’ decisions: Lessons from two crises, Working Paper .
Becker, Bo, and Victoria Ivashina, 2014a, Cyclicality of credit supply: Firm level evidence,
Journal of Monetary Economics 62, 76–93.
Becker, Bo, and Victoria Ivashina, 2014b, Financial repression in the european sovereign
debt crisis, Working Paper .
Bentolila, Samuel, Marcel Jansen, Gabriel Jimenez, and Sonia Ruano, 2013, When credit
dries up: Job losses in the great recession, CESifo Working Paper .
Bernanke, Ben, 1983, Nonmonetary effects of the financial crisis in the propagation of the
great depression, American Economic Review 73, 257–276.
Bocola, Luigi, 2014, The pass-through of sovereign risk, Working Paper .
42
Bofondi, Marcello, Luisa Carpinelli, and Enrico Sette, 2013, Credit supply during a sovereign
debt crisis, Bank of Italy Temi di Discussione Working Paper .
Chodorow-Reich, Gabriel, 2014, The employment effects of credit market disruptions: Firm-
level evidence from the 2008-09 financial crisis, Quarterly Journal of Economics 129, 1–59.
Cingano, Federico, Francesco Manaresi, and Enrico Sette, 2013, Does credit crunch invest-
ments down? new evidence on the real effects of the bank-lending channel, Working Paper
.
Crosignani, Matteo, 2014, Why are banks not recapitalized during crises?, Working Paper .
De Marco, Filippo, 2014, Bank lending and the sovereign debt crisis, Working Paper .
De Marco, Filippo, and Marco Macchiavelli, 2014, The political origin of home bias: The
case of europe, Working Paper .
Diamond, Douglas W, 1991, Monitoring and reputation: The choice between bank loans and
directly placed debt, Journal of Political Economy 99, 689–721.
Diamond, Douglas W, and Raghuram G Rajan, 2011, Fear of fire sales, illiquidity seeking,
and credit freezes, The Quarterly Journal of Economics 126, 557–591.
Dombret, Andreas, and Patrick S Kenadjian, 2015, The European Capital Markets Union:
A viable concept and a real goal?, volume 17 (Walter de Gruyter GmbH & Co KG).
Drechsler, Itamar, Thomas Drechsel, David Marques-Ibanez, and Philipp Schnabl, 2014,
Who borrows from the lender of last resort?, Working Paper .
Farhi, Emmanuel, and Jean Tirole, 2014, Deadly embrace: Sovereign and financial balance
sheets doom loops, Working Paper .
Iannotta, Giuliano, Giacomo Nocera, and Andrea Sironi, 2013, The impact of government
ownership on bank risk, Journal of Financial Intermediation 22, 152–176.
Imbens, Guido M, and Jeffrey M Wooldridge, 2009, Recent developments in the econometrics
of program evaluation, Journal of Economic Literature 47, 5–86.
43
Ivashina, Victoria, 2009, Asymmetric information effects on loan spreads, Journal of
Financial Economics 92, 300–319.
Ivashina, Victoria, David S Scharfstein, and Jeremy C Stein, 2015, Dollar funding and the
lending behavior of global banks, The Quarterly Journal of Economics qjv017.
Khwaja, Asim Ijaz, and Atif Mian, 2008, Tracing the impact of bank liquidity shocks: Evi-
dence from an emerging market, American Economic Review 98, 1413–1442.
Mian, Atif, and Amir Sufi, 2014, What explains the 20072009 drop in employment?,
Econometrica 82, 2197–2223.
Peek, Joe, and Eric S Rosengren, 1997, The international transmission of financial shocks:
The case of japan, American Economic Review 87, 495–505.
Popov, Alexander, and Neeltje Van Horen, 2014, Exporting sovereign stress: Evidence from
syndicated bank lending during the euro area sovereign debt crisis, Review of Finance
forthcoming.
Standard&Poor’s, 2006, Corporate Ratings Criteria ((New York, NY: The McGraw-Hill
Companies, Inc.)).
Standard&Poor’s, 2010, A Guide To The European Loan Market.
Sufi, Amir, 2007, Information asymmetry and financing arrangements: Evidence from syn-
dicated loans, Journal of Finance 62, 629–668.
Uhlig, Harald, 2014, Sovereign default risk and banks in a monetary union, German
Economic Review 15, 23–41.
44
Table I - Variable Definitions
Variable Definition
Dependent Variables (winsorized at the 5% level)
Net Debt Current + Non-Current Liabilities - CashTotal Assets
∆Cash Cash t+1−Cash t
Total Assets t
Interest Coverage Ratio EBITInterest Expense
Employment Growth ln(Employmentt+1)− ln(Employmentt)
CAPXFixed Assets t+1−Fixed Assets t+Depreciation
Fixed Assets t, set to 0 if negative
Sales Growth ln(Salest+1)− ln(Salest)
Key Explanatory Variables
Crisisbt Dummy equal to one if bank b’s country of incorporation is in crisis period at time t
GIIPS Bank Dependence ijht
∑l∈Lijh,min{t,ti}
∑b∈l GIIPS b
#Lead Arranger l
·Loan Amount l
Total Loan Amount ijh,min{t,ti}
GIIPS Bank Dependence in Crisis ijht
∑l∈Lijhti
∑b∈l GIIPS b·Crisis bt
#Lead Arranger l
·Loan Amount l
Total Loan Amount ijhti
Affected Bank Dependence ijht
∑l∈Lijh,min{t,ti}
∑b∈l Affected b
#Lead Arranger l
·Loan Amount l
Total Loan Amount ijh,min{t,ti}
Affected GIIPS Bank Dependence ijht
∑l∈Lijh,min{t,ti}
∑b∈l Affected b·GIIPS b
#Lead Arranger l
·Loan Amount l
Total Loan Amount ijh,min{t,ti}
Affected GIIPS Bank Dependence in Crisis ijht
∑l∈Lijhti
∑b∈l Affected b·GIIPS b·Crisis bt
#Lead Arranger l
·Loan Amount l
Total Loan Amount ijhti
Affected Bank Measures
CDS Weighted GIIPS Sov. Bondholdings Banks with an above median ratio of∑
j Sov. Bondholdingsjt·CDSjt
Total Assetst, ∀j ∈ GIIPS
CDS Weighted Domestic Sov. Bondholdings Banks with an above median ratio ofDomestic Sov. Bondholdings t·Domestic Sov. CDS t
Total Assets t
High Leverage Banks with a below median ratio ofTotal EquityTotal Assets
Low Rating Banks with a rating of A+ or worse
Gov. Intervention Banks that received government support during the sovereign debt crisis
High Fraction Gov. Own. Banks with an above median fraction of government ownership
High Fraction Gov. Board Banks with an above median fraction of government affiliated directors on the board
Control Variables (winsorized at the 5% level)
ln(Assets) Natural logarithm of total assets
LeverageTotal Assets-Total Equity
Total Assets
Net WorthTotal shareholder funds&Liabilities - Current&Non-Current Liabilities - Cash
Total Assets
Tangibility Fixed AssetsTotal Assets
EBITDA/Assets EBITDATotal Assets
Cash FlowCash flow
Total Assets
45
Tab
leII
-D
escr
ipti
veSta
tist
ics
pre
-Cri
sis
Panel
A:
Dep
endent
Vari
able
sP
anel
B:
Expla
nato
ryV
ari
able
s
Em
p.
Gro
wth
CA
PX
Sal
esG
row
thT
otal
Ass
ets
(mn)
Tan
gib
ilit
yIn
t.C
ov.
Net
Wor
thE
BIT
DA
/Ass
ets
Lev
erag
e
Mea
n0.
054
0.19
50.
057
4330
0.61
02.
980.
220
0.10
80.
620
Hig
hG
IIP
SB
ank
Dep
.M
edia
n0.
033
0.11
60.
056
737
0.63
21.
940.
206
0.10
40.
619
Std
.D
ev.
0.15
70.
243
0.22
177
100.
211
3.32
0.17
40.
075
0.19
8
Mea
n0.
045
0.19
20.
049
2460
0.54
73.
240.
227
0.11
50.
604
Low
GII
PS
Ban
kD
ep.
Med
ian
0.02
10.
112
0.05
241
60.
557
2.06
0.23
30.
104
0.59
2
Std
.D
ev.
0.16
20.
249
0.20
553
700.
240
3.50
0.18
70.
098
0.25
6
Diff
.0.
009
0.00
30.
007
3050
0.06
2-0
.267
-0.0
07-0
.006
20.
163
(t-S
tat)
(1.1
4)(0
.25)
(0.6
9)(7
.08)
(5.5
5)(-
1.58
)(-
0.84
)(-
1.41
)(1
.41)
Nor
mal
ized
Diff
.0.
242
0.19
7-0
.053
-0.0
27-0
.056
0.04
9
Cor
rela
tion
wit
hG
IIP
SB
ank
Dep
.-0
.037
0.08
7-0
.116
-0.0
64-0
.127
0.11
7
Pan
elA
pre
sents
des
crip
tive
stat
isti
csof
dep
end
ent
vari
ab
les
wh
ile
Pan
elB
pre
sents
exp
lan
ato
ryva
riab
les
spli
tin
tofi
rms
wit
ha
hig
han
dlo
wGIIPSBankDepen
den
ce.
Hig
h(l
ow)GIIPSBankDepen
den
ceis
an
ind
icato
rva
riab
leeq
ual
toon
eif
the
fract
ion
of
tota
lou
tsta
nd
ing
loan
sto
afi
rmp
rovid
edby
GII
PS
lead
arr
an
ger
sis
ab
ove
(bel
ow)
the
sam
ple
med
ian
.T
he
sam
ple
con
sist
sof
all
firm
sin
the
inte
rsec
tion
ofD
ealS
can
and
Am
adeu
sth
at
are
loca
ted
in:
Gre
ece,
Italy
,Ir
elan
d,
Port
ugal,
Sp
ain
(GII
PS
cou
ntr
ies)
or
Ger
man
y,F
ran
ce,
U.K
.(n
on-G
IIP
Sco
untr
ies)
.
46
Tab
leII
-D
escr
ipti
veSta
tist
ics
(con
td.)
Pan
el
C:
Non-G
IIP
Sfirm
sw
ithout
GII
PS
or
oth
er
non-E
Usu
bsi
dia
ries
Tot
alA
sset
s(m
n)
Tan
gibilit
yIn
t.C
ov.
Net
Wor
thE
BIT
DA
/Ass
ets
Lev
erag
e
Mea
n63
300.
580
1.96
80.
200
0.08
70.
664
Hig
hG
IIP
SB
ank
Dep
.M
edia
n13
700.
527
1.34
00.
182
0.08
50.
673
Std
.D
ev.
1020
00.
223
2.54
60.
132
0.04
80.
139
Mea
n87
100.
559
1.93
90.
210
0.10
10.
646
Low
GII
PS
Ban
kD
ep.
Med
ian
2560
0.55
81.
300
0.18
00.
099
0.67
8
Std
.D
ev.
1200
00.
167
2.54
00.
143
0.06
20.
128
Diff
.(t
-Sta
t)-2
380
(-1.
37)
-0.0
208
(-0.
68)
-0.0
28(-
0.07
)0.
010
(0.5
0)-0
.014
5(-
1.35
)0.
0177
(0.8
4)
Nor
mal
ized
Diff
.-0
.151
-0.0
75-0
.008
0.05
1-0
.178
0.09
5
Cor
rela
tion
wit
hG
IIP
SB
ank
Dep
.-0
.07
-0.0
30
0.01
-0.0
9-0
.02
Pan
el
D:
GII
PS
firm
sw
ith
hig
hfr
act
ion
of
revenue
genera
ted
by
non-G
IIP
Ssu
bsi
dia
ries
Mea
n10
800.
536
3.50
80.
208
0.11
80.
662
Hig
hG
IIP
SB
ank
Dep
.M
edia
n49
50.
570
2.54
00.
216
0.10
60.
625
Std
.D
ev.
2980
0.24
03.
358
0.20
90.
070
0.22
8
Mea
n13
100.
564
2.85
50.
210
0.10
50.
655
Low
GII
PS
Ban
kD
ep.
Med
ian
233
0.57
91.
855
0.19
90.
093
0.63
0
Std
.D
ev.
3510
0.28
43.
036
0.22
50.
082
0.29
5
Diff
.(t
-Sta
t)-2
24(-
0.47
)0.
027
(0.7
0)-0
.653
(-1.
52)
-0.0
01(-
0.04
)-0
.012
(-1.
11)
0.00
6(0
.16)
Nor
mal
ized
Diff
.0.
049
0.07
5-0
.142
-0.0
06-0
.120
0.01
8
Cor
rela
tion
wit
hG
IIP
SB
ank
Dep
.-0
.037
0.08
7-0
.092
-0.0
64-0
.127
0.16
9
Pan
els
Can
dD
pre
sent
des
crip
tive
stat
isti
csof
exp
lan
ato
ryva
riab
les
for
the
pre
-cri
sis
per
iod
.P
an
elC
rep
ort
ssu
mm
ary
stati
stic
sfo
rn
on-G
IIP
Sfi
rms
wit
hou
tG
IIP
Sor
oth
ern
on-E
Usu
bsi
dia
ries
an
dP
an
elD
rep
ort
sre
sult
sfo
rG
IIP
Sfi
rms
wit
ha
hig
hfr
act
ion
of
reve
nu
ege
ner
ated
by
non
-GII
PS
sub
sid
iari
es.
Both
panel
sare
spli
tin
tofi
rms
wit
hh
igh
an
dlo
wGIIPSBankDepen
den
ce(s
ub
sam
ple
spec
ific
cuto
ffp
oints
are
use
dto
clas
sify
firm
sas
hig
hor
low
GIIPSBankDepen
den
ce).
Pan
elC
incl
ud
esfi
rms
loca
ted
inG
erm
any,
Fra
nce
,or
U.K
.(n
on-G
IIP
Sco
untr
ies)
that
do
not
hav
esu
bsi
dia
ries
loca
ted
inG
reec
e,It
aly
,Ir
elan
d,
Port
ugal,
or
Sp
ain
(GII
PS
cou
ntr
ies)
oran
yot
her
non
-EU
cou
ntr
y.P
anel
Din
clu
des
firm
sin
GII
PS
cou
ntr
ies
that
hav
ea
hig
hfr
act
ion
of
thei
rre
venu
esge
ner
ated
by
non
-GII
PS
sub
sid
iari
es.
47
Tab
leII
-D
escr
ipti
veSta
tist
ics
(con
td.)
Panel
E:
GII
PS
vs.
non-G
IIP
SB
anks
Tot
alA
sset
s(m
n)
Equit
y/A
sset
sIm
pai
red
Loa
ns/
Equit
yT
ier1
Rat
ioA
vg
5-ye
arC
DS
Spre
ad
Mea
n19
2330
0.06
40.
376
0.08
560
.79
GII
PS
Ban
ks
Med
ian
8037
80.
062
0.32
50.
080
60.9
5
Std
.D
ev.
2603
560.
018
0.27
10.
025
18.3
4
Mea
n65
8094
0.03
00.
412
0.08
760
.27
Non
-GII
PS
Ban
ks
Med
ian
4129
770.
027
0.35
10.
086
44.4
9
Std
.D
ev.
6589
260.
013
0.27
60.
018
45.0
7
Diff
.(t
-Sta
t)-4
6576
3(-
6.07
)0.
034
(13.
03)
-0.0
35(0
.76)
-0.0
01(-
0.51
)0.
5(0
.04)
Nor
mal
ized
Diff
.-0
.657
1.53
1-0
.093
-0.0
640.
017
Panel
F:
Non-G
IIP
SB
anks
Mea
n71
0191
0.03
00.
350.
087
63.6
2
Hig
hfr
acti
onG
IIP
Ssy
ndic
ates
Med
ian
4196
540.
025
0.27
0.08
745
.24
Std
.D
ev.
6220
900.
013
0.25
0.01
854
.18
Mea
n57
3659
0.03
40.
480.
088
55.0
0
Low
frac
tion
GII
PS
syndic
ates
Med
ian
2231
650.
033
0.43
0.08
242
.72
Std
.D
ev.
7178
220.
010
0.29
0.01
928
.46
Diff
.(t
-Sta
t)13
6532
(0.8
7)-0
.004
(-1.
55)
-0.0
12(-
1.81
)-0
.001
(-0.
12)
-8.6
1(-
0.38
)
Nor
mal
ized
Diff
.0.
178
-0.2
43-0
.336
-0.0
38-0
.140
Pan
els
Ean
dF
pre
sent
des
crip
tive
stat
isti
csfo
rth
eb
an
ks
inou
rsa
mp
lein
the
pre
-cri
sis
per
iod
.P
an
elE
com
pare
sG
IIP
San
dn
on
-G
IIP
Sb
anks,
wh
ile
Pan
elF
com
par
esn
on-G
IIP
Sb
an
ks
wit
han
ab
ove
an
db
elow
med
ian
fract
ion
of
dea
lsis
sued
wit
hG
IIP
SB
an
ks.
Non
-GII
PS
ban
ks
are
hea
dqu
arte
red
inG
erm
any,
Fra
nce
,or
U.K
.(n
on
-GII
PS
cou
ntr
ies)
,w
her
eas
GII
PS
ban
ks
are
hea
dqu
art
ered
inG
reec
e,It
aly,
Irel
and
,P
ortu
gal,
orS
pai
n(G
IIP
Sco
untr
ies)
.
48
Tab
leII
I-
Rea
lan
dF
inan
cial
Outc
omes
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Net
Deb
t∆
Cas
hIn
t.C
ov.
TotalC
red
itLin
eCash
+TotalC
red
itLin
eUndrawnCred
itLin
eCash
+UndrawnCred
itLin
eE
mp.
Gro
wth
CA
PX
Sal
esG
row
th
GII
PS
Ban
kD
ep.
inC
risi
s-0
.039
***
0.00
3-0
.023
***
-0.0
91**
-0.1
64**
*-0
.041
***
-0.0
60**
*-0
.049
***
(-2.
98)
(0.7
3)(-
2.82
)(-
2.31
)(-
3.33
)(-
2.97
)(-
2.70
)(-
2.96
)
Cas
hF
low
*GII
PS
Ban
kD
ep.
-0.0
03
(-0.
50)
Cas
hF
low
*GII
PS
Ban
kD
ep.
inC
risi
s0.
007*
*
(2.4
4)
Cas
hF
low
0.00
0
(0.1
0)
R2
0.54
30.
442
0.39
50.
831
0.84
10.
423
0.58
70.
494
N44
4840
0347
1050
750
737
8143
5142
14
Fir
mC
ontr
ols
YE
SY
ES
YE
SY
ES
YE
SY
ES
YE
SY
ES
Fir
mF
ixed
Eff
ects
YE
SY
ES
YE
SY
ES
YE
SY
ES
YE
SY
ES
Indust
ry*C
ountr
y*Y
ear
Fix
edE
ffec
tsY
ES
YE
SY
ES
YE
SY
ES
YE
SY
ES
YE
S
For
eign
Ban
kC
ountr
y*Y
ear
Fix
edE
ffec
tsY
ES
YE
SY
ES
NO
NO
YE
SY
ES
YE
S
Tab
leII
Ip
rese
nts
firm
-lev
elre
gres
sion
resu
lts.
Th
ed
epen
den
tva
riab
les
are
net
deb
t,th
ech
an
ge
inca
shh
old
ings,
inte
rest
cove
rage
rati
o,cr
edit
lin
esra
tio,
un
dra
wn
cred
itli
nes
rati
o,
emp
loym
ent
gro
wth
,in
vest
men
ts,
an
dsa
les
gro
wth
,re
spec
tive
ly.
Th
esa
mp
leco
nsi
sts
ofal
lfi
rms
inth
ein
ters
ecti
onof
Dea
lSca
nan
dA
mad
eus
that
are
loca
ted
in:
Gre
ece,
Italy
,Ir
elan
d,
Port
ugal,
Sp
ain
(GII
PS
cou
ntr
ies)
orG
erm
any,
Fra
nce
,U
.K.
(non
-GII
PS
cou
ntr
ies)
for
the
emp
loym
ent
gro
wth
,in
vest
men
ts,
sale
sgro
wth
,n
etd
ebt
an
dca
shfl
owre
gres
sion
s.F
orth
ecr
edit
lin
ere
gres
sion
s,th
esa
mp
leco
nsi
sts
of
all
firm
sin
the
inte
rsec
tion
of
Dea
lSca
n,
Am
ad
eus,
an
dC
ap
ital
IQth
atar
elo
cate
din
aG
IIP
Sor
non
-GII
PS
cou
ntr
y.GIIPSBankDepen
den
ceis
the
fract
ion
of
tota
lou
tsta
nd
ing
loan
sp
rovid
edby
GII
PS
lead
arra
nge
rs.GIIPSBankDepen
den
cein
Crisis
isth
efr
act
ion
of
tota
lou
tsta
nd
ing
loan
sp
rovid
edby
GII
PS
lead
arr
an
ger
sth
atar
ein
corp
orat
edin
acr
isis
cou
ntr
yin
year
t,w
her
eth
ecr
isis
beg
ins
inG
reec
ein
2009
an
din
2010
inth
eoth
erG
IIP
Sco
untr
ies.
Fir
mco
ntr
olva
riab
les
incl
ud
eth
elo
gari
thm
ofto
tal
ass
ets,
tan
gib
ilit
y,in
tere
stco
ver
age
rati
o(n
ot
inC
olu
mn
(3))
,E
BIT
DA
/to
tal
asse
ts,
lever
age,
net
wor
than
dfo
rth
eca
shre
gres
sion
afi
rm’s
cash
flow
an
dca
pit
al
exp
end
itu
res.
All
firm
-lev
elco
ntr
ol
vari
ab
les
are
lagg
edby
one
per
iod
.A
llva
riab
les
are
defi
ned
inT
ab
leI.
All
regre
ssio
ns
incl
ud
efi
rman
din
du
stry
-cou
ntr
y-y
ear
fixed
effec
ts,
as
wel
las
all
firm
-lev
elco
ntr
ols.
Col
um
ns
(1)-
(2)
and
(5)-
(7)
add
itio
nall
yin
clu
de
fore
ign
ban
kco
untr
y-y
ear
fixed
effec
ts.
Sta
nd
ard
erro
rsar
ead
just
edfo
rh
eter
osce
das
tici
tyan
dcl
ust
ered
at
the
firm
-lev
el.
Sig
nifi
can
cele
vels
:*
(p<
0.10),
**
(p<
0.0
5),
***
(p<
0.01).
49
Table IV - Subsidiaries
(1) (2) (3) (4) (5) (6)
Net Debt ∆Cash Int. Cov. Emp. Growth CAPX Sales Growth
Panel A: Non-GIIPS Firms without GIIPS or other non-EU Subsidiaries
GIIPS Bank Dep. in Crisis -0.061** -0.037 -0.056** -0.097*** -0.093** -0.074**(-2.59) (-1.14) (-2.01) (-2.95) (-2.07) (-2.16)
Cash Flow*GIIPS Bank Dep. in Crisis 0.177***(3.12)
R2 0.496 0.475 0.370 0.419 0.583 0.443N 1175 997 1272 892 1107 1079
Panel B: Non-GIIPS Firms without GIIPS Subsidiaries (M&A only)
GIIPS Bank Dep. in Crisis -0.038* 0.009 -0.045** -0.079*** -0.090*** -0.078**(-1.93) (0.30) (-2.54) (-3.54) (-3.26) (-2.22)
Cash Flow*GIIPS Bank Dep. in Crisis 0.161**(2.14)
R2 0.503 0.496 0.376 0.435 0.585 0.449N 1066 895 1136 806 999 976
Panel C: Non-GIIPS Firms without GIIPS Subsidiaries (non-tradable sectors)
GIIPS Bank Dep. in Crisis -0.041** -0.048 -0.059** -0.089** -0.061* -0.083***(-2.41) (-1.19) (-2.16) (-2.10) (-1.79) (-2.65)
Cash Flow*GIIPS Bank Dep. in Crisis 0.182***(3.58)
R2 0.546 0.507 0.448 0.456 0.624 0.480N 1007 864 1082 743 942 919
Panel D: Non-GIIPS Firms with GIIPS Subsidiaries
GIIPS Bank Dep. in Crisis -0.002 -0.005 -0.017 -0.027* -0.010 -0.014(-0.10) (-0.91) (-1.24) (-1.93) (-0.48) (-0.74)
Cash Flow*GIIPS Bank Dep. in Crisis 0.041**(2.49)
R2 0.561 0.379 0.301 0.344 0.600 0.446N 1315 1282 1333 1192 1304 1302
Firm Controls YES YES YES YES YES YESFirm Fixed Effects YES YES YES YES YES YESIndustry*Year Fixed Effects YES YES YES YES YES YESForeign Bank Country*Year Fixed Effects YES YES YES YES YES YES
Table IV presents firm-level regression results. The dependent variables are net debt, the change in cashholdings, interest coverage ratio, employment growth, investments, and sales growth, respectively. Thesample consists of firms in the intersection of DealScan and Amadeus. Panel A–C only include firms inGermany, France, or U.K. (non-GIIPS countries) that do not have subsidiaries in Greece, Italy, Ireland,Portugal, or Spain (GIIPS countries) or any other non-EU country. Panel B restricts the sample further tofirms that have their GIIPS bank relationships because of bank M&As, whereas Panel C restricts it to firmsactive in non-tradable sectors. Panel D includes only firms in non-GIIPS countries that have at least oneGIIPS subsidiary. GIIPS Bank Dependence is the fraction of total outstanding loans provided by GIIPS leadarrangers. GIIPS Bank Dependence in Crisis is the fraction of total outstanding loans provided by GIIPSlead arrangers that are incorporated in a crisis country in year t, where the crisis begins in Greece in 2009 andin 2010 in the other GIIPS countries. Firm control variables include the logarithm of total assets, leverage,net worth, tangibility, interest coverage ratio (not in Column (3)), and EBITDA/total assets and for thecash regression a firm’s cash flow and capital expenditures. All firm-level control variables are lagged byone period. All variables are defined in Table I. All regressions include firm, industry-year, and foreign bankcountry-year fixed effects, as well as all firm-level controls. Standard errors are adjusted for heteroscedasticityand clustered at the firm-level. Significance levels: * (p < 0.10), ** (p < 0.05), *** (p < 0.01).
50
Table V - Firms’ Ability to substitute Loan Supply Decrease
(1) (2) (3) (4) (5) (6)Net Debt ∆Cash Int. Cov. Emp. Growth CAPX Sales Growth
Panel A: Listed FirmsGIIPS Bank Dep. in Crisis 0.013 0.013* -0.004 -0.038 -0.018 -0.037
(0.68) (1.95) (-0.28) (-1.41) (-0.62) (-1.36)Cash Flow*GIIPS Bank Dep. in Crisis -0.005
(-1.41)R2 0.669 0.569 0.482 0.552 0.673 0.648N 1805 1772 1832 1737 1786 1748Panel B: Non-listed FirmsGIIPS Bank Dep. in Crisis -0.045** 0.003 -0.039** -0.047** -0.073** -0.056**
(-2.31) (0.50) (-2.52) (-2.20) (-2.12) (-2.06)Cash Flow*GIIPS Bank Dep. in Crisis 0.010**
(2.46)R2 0.637 0.558 0.540 0.548 0.678 0.592N 2643 2231 2878 2044 2565 2466Panel C: Rated FirmsGIIPS Bank Dep. in Crisis -0.037 0.033 -0.002 -0.033 -0.056 -0.043
(-1.15) (1.59) (-0.02) (-1.10) (-1.50) (-1.22)Cash Flow*GIIPS Bank Dep. in Crisis -0.034
(-1.26)R2 0.763 0.787 0.7344 0.739 0.764 0.826N 572 562 586 539 565 546Panel D: Unrated FirmsGIIPS Bank Dep. in Crisis -0.043*** 0.005 -0.030*** -0.043*** -0.070*** -0.050***
(-2.87) (0.98) (-2.87) (-2.71) (-2.96) (-2.63)Cash Flow*GIIPS Bank Dep. in Crisis 0.008***
(2.75)R2 0.568 0.468 0.4241 0.461 0.614 0.502N 3876 3441 4124 3242 3786 3668Panel E: Firms with Bond IssuesGIIPS Bank Dep. in Crisis -0.012 -0.001 0.015 -0.028 -0.099 -0.056
(-0.25) (-0.07) (1.12) (-0.56) (-1.40) (-0.71)Cash Flow*GIIPS Bank Dep. in Crisis -0.004
(-0.10)R2 0.843 0.836 0.801 0.798 0.833 0.764N 354 334 363 328 349 339Panel F: Firms without Bond IssuesGIIPS Bank Dep. in Crisis -0.043*** 0.004 -0.028*** -0.040*** -0.067*** -0.043**
(-2.88) (0.84) (-2.99) (-2.61) (-2.79) (-2.35)Cash Flow*GIIPS Bank Dep. in Crisis 0.007**
(2.39)R2 0.552 0.444 0.412 0.439 0.596 0.493N 4094 3669 4347 3453 4002 3875
Table V presents firm-level regression results. The dependent variables are net debt, the change in cashholdings, interest coverage ratio, employment growth, investments, and sales growth, respectively. Thesample consists of all firms in the intersection of DealScan and Amadeus and located in: Greece, Italy,Ireland, Portugal, Spain (GIIPS countries) or Germany, France, U.K. (non-GIIPS countries). Panel A (B)includes listed (non-listed) firms. Panel C (D) includes rated (unrated) firms. Panel E (F) includes firmsthat did (did not) issue bonds during the sample period. GIIPS Bank Dependence is the fraction of totaloutstanding loans provided by GIIPS lead arrangers. GIIPS Bank Dependence in Crisis is the fraction oftotal outstanding loans provided by GIIPS lead arrangers that are incorporated in a crisis country in year t,where the crisis begins in Greece in 2009 and in 2010 in the other GIIPS countries. Firm control variablesinclude the logarithm of total assets, leverage, net worth, tangibility, interest coverage ratio (not in Column(3)), and EBITDA/total assets and for the cash regression a firm’s cash flow and capital expenditures. Allfirm-level control variables are lagged by one period. All variables are defined in Table I. All regressionsinclude firm, industry-country-year, and foreign bank country-year fixed effects (not Panel C and E), as wellas all firm-level controls. Standard errors are adjusted for heteroscedasticity and clustered at the firm-level.Significance levels: * (p < 0.10), ** (p < 0.05), *** (p < 0.01).
51
Table VI - Switcher vs. non-Switcher
(1) (2) (3) (4) (5) (6)
Net Debt ∆Cash Int. Cov. Emp. Growth CAPX Sales Growth
Panel A: Firms with constant GIIPS Bank Dependence
GIIPS Bank Dep. in Crisis -0.044*** 0.002 -0.021* -0.043** -0.074*** -0.047**
(-2.74) (0.28) (-1.94) (-2.29) (-2.81) (-2.12)
Cash Flow*GIIPS Bank Dep. in Crisis 0.011***
(3.35)
R2 0.598 0.482 0.423 0.476 0.647 0.525
N 3405 3016 3607 2795 3326 3237
Panel B: Firms that switch their Bank Relationships
GIIPS Bank Dep. in Crisis -0.011 0.002 -0.018 -0.014 -0.018 0.008
(-0.44) (0.29) (-0.88) (-0.58) (-0.54) (0.19)
Cash Flow*GIIPS Bank Dep. in Crisis 0.002
(0.22)
R2 0.781 0.720 0.711 0.753 0.761 0.772
N 1043 987 1103 986 1025 977
Panel C: Switcher vs. non-Switcher
Listed Non-Listed Cum.
Switcher 62.38% 37.62% 100%
Non-Switcher 27.27% 72.73% 100%
Table VI presents firm-level regression results. The dependent variables are net debt, the change in cashholdings, interest coverage ratio, employment growth, investments, and sales growth, respectively. Thesample consists of all firms in the intersection of DealScan and Amadeus and located in: Greece, Italy,Ireland, Portugal, Spain (GIIPS countries) or Germany, France, U.K. (non-GIIPS countries). Panel Aincludes firms that have a constant GIIPS Bank Dependence throughout the sample period, whereas PanelB firms that switch their bank relationships. Finally, Panel C reports the fraction of firms with constantGIIPS Bank Dependence (non-switcher) in the listed and non-listed subsamples. GIIPS Bank Dependence isthe fraction of total outstanding loans provided by GIIPS lead arrangers. GIIPS Bank Dependence in Crisisis the fraction of total outstanding loans provided by GIIPS lead arrangers that are incorporated in a crisiscountry in year t, where the crisis begins in Greece in 2009 and in 2010 in the other GIIPS countries. Firmcontrol variables include the logarithm of total assets, leverage, net worth, tangibility, interest coverage ratio(not in Column (3)), and EBITDA/total assets and for the cash regression a firm’s cash flow and capitalexpenditures. All firm-level control variables are lagged by one period. All variables are defined in TableI. All regressions include firm, industry-country-year, and foreign bank country-year fixed effects, as well asall firm-level controls. Standard errors are adjusted for heteroscedasticity and clustered at the firm-level.Significance levels: * (p < 0.10), ** (p < 0.05), *** (p < 0.01).
52
Table VII - Lending Volume and Spread
(1) (2) (3) (4) (5) (6) (7)∆Volume ∆Volume ∆Volume ∆Volume ∆Volume ∆Volume ∆Volume
Panel A: Loan Volume
GIIPS*Crisis -0.046** -0.018 -0.045* -0.068** -0.046* -0.039*(-2.04) (-0.71) (-1.74) (-2.12) (-1.78) (-1.66)
CDS Weighted Dom. Bondholdings*Crisis -0.048**(-2.00)
High Leverage*GIIPS*Crisis -0.076**(-2.04)
Low Rating*GIIPS*Crisis -0.096**(-1.97)
Gov. Intervention*GIIPS*Crisis 0.044(1.08)
High Gov. Board*GIIPS*Crisis -0.012(-0.37)
High Gov. Own.*GIIPS*Crisis -0.045(-1.12)
R2 0.707 0.744 0.731 0.730 0.727 0.730 0.730N 5448 4947 5372 5372 5372 5372 5372
Panel B: Loan Spread
∆Spread ∆Spread ∆Spread ∆Spread ∆Spread ∆Spread ∆Spread
GIIPS*Crisis 0.041* 0.018 0.045* 0.043 0.044* 0.052**(1.92) (1.06) (1.84) (0.98) (1.69) (2.21)
CDS Weighted Dom. Bondholdings*Crisis 0.047*(1.92)
High Leverage*GIIPS*Crisis 0.082**(2.03)
Low Rating*GIIPS*Crisis 0.157*(1.93)
Gov. Intervention*GIIPS*Crisis -0.022(-0.45)
High Gov. Board*GIIPS*Crisis -0.075(-1.14)
High Gov. Own.*GIIPS*Crisis -0.072(-1.07)
R2 0.685 0.737 0.747 0.747 0.745 0.748 0.748N 3230 3004 3171 3171 3171 3171 3171
Bank-Level Controls YES YES YES YES YES YES YESFirm Cluster-Year Fixed Effects YES YES YES YES YES YES YESFirm Cluster-Bank Fixed Effects YES YES YES YES YES YES YES
Table VII presents the results of a modified version of the Khwaja and Mian (2008) bank lending channelregression. The unit of observation is a firm cluster-bank-year. The dependent variable is the change inlog loan volume (Panel A) or change in log spread (Panel B) of a bank-firm cluster relation in a given yearwhere firm clusters are formed based on a firm’s country of incorporation, industry, and rating. The ratingof each firm is estimated from EBIT interest coverage ratio medians for firms by rating category providedby Standard & Poor’s. We assign ratings on the basis of the pre-crisis median interest coverage ratio ofeach firm. Data are restricted to: (i) the set of firm cluster-bank relations that existed before the startof the sovereign debt crisis, and (ii) firm cluster-bank years where firms in a cluster borrow from at leastone GIIPS bank and one non-GIIPS bank. Crisis is an indicator variable equal to one starting in 2009 forbanks incorporated Greece and in 2010 for banks incorporated in all other GIIPS countries (beginning of thesovereign debt crisis) and zero before. All variables are defined in Table I. All regressions include firm cluster-year fixed effects, firm cluster-bank fixed effects, and bank controls (logarithm of total assets, equity/totalassets, impaired loans/total equity). Standard errors are clustered at the bank level. Significance levels: *(p < 0.10), ** (p < 0.05), *** (p < 0.01).
53
Table
VII
I-
Pas
sive
Chan
nel
:H
iton
Bal
ance
Shee
t
Pan
el
A:
Ris
kof
GII
PS
Sovere
ign
Bon
dh
old
ings
(1)
(2)
(3)
(4)
(5)
(6)
Net
Deb
t∆
Cas
hIn
t.C
ov.
Em
p.
Gro
wth
CA
PX
Sal
esG
row
th
CD
SW
eigh
ted
GII
PS
Sov
.B
ond
hol
din
gsin
Cri
sis
-0.0
18**
-0.0
00-0
.010
**-0
.025
**-0
.029
**-0
.031
*
(-2.
02)
(-0.
01)
(-2.
15)
(-2.
03)
(-2.
07)
(-1.
80)
CD
SW
eigh
ted
GII
PS
Sov
.B
ond
hol
din
gsin
Cri
sis*
Cas
hF
low
0.01
1***
(2.7
3)
R2
0.55
30.
472
0.42
30.
441
0.60
90.
516
N41
9137
8143
6935
7340
9939
72
Pan
el
B:
Ris
kof
Dom
est
icS
overe
ign
Bon
dh
old
ings
CD
SW
eigh
ted
Dom
esti
cS
ov.
Bon
dh
old
ings
inC
risi
s-0
.019
**0.
000
-0.0
11**
-0.0
27**
-0.0
31**
-0.0
27**
(-2.
24)
(0.1
4)(-
2.21
)(-
2.23
)(-
2.23
)(-
2.22
)
CD
SW
eigh
ted
Dom
esti
cS
ov.
Bon
dh
old
ings
inC
risi
s*C
ash
Flo
w0.
011*
**
(2.8
1)
R2
0.55
30.
474
0.42
20.
609
0.50
2
N41
9137
8143
6935
7340
9939
72
Tab
leV
III
pre
sents
firm
-lev
elre
gres
sion
resu
lts.
Th
ed
epen
den
tva
riab
les
are
net
deb
t,th
ech
an
ge
inca
shh
old
ings,
inte
rest
cove
rage
rati
oem
plo
ym
ent
grow
th,
inve
stm
ents
,an
dsa
les
gro
wth
,re
spec
tive
ly.
The
sam
ple
con
sist
sof
all
firm
sin
the
inte
rsec
tion
of
Dea
lSca
nan
dA
mad
eus
that
are
loca
ted
in:
Gre
ece,
Ital
y,Ir
elan
d,
Port
ugal,
Sp
ain
(GII
PS
cou
ntr
ies)
or
Ger
many,
Fra
nce
,U
.K.
(non
-GII
PS
cou
ntr
ies)
,w
hic
hh
ave
ale
nd
ing
rela
tion
ship
wit
ha
ban
kth
at
was
part
of
the
EB
Ast
ress
test
s.CDS
Weighted
GIIPS
Sov.
Bondholdings
inCrisis
(Pan
elA
)an
dCDS
WeightedDomesticSov.
Bondholdings
inCrisis
(Pan
elB
)m
easu
reth
efr
act
ion
of
affec
ted
lead
arra
nge
rsb
ased
onth
eri
sk-a
dju
sted
GII
PS
an
dd
om
esti
cso
ver
eign
bon
dh
old
ings
of
ban
ks
ina
firm
’slo
an
syn
dic
ate
,re
spec
tive
ly,
that
are
inco
rpor
ated
ina
cris
isco
untr
yin
yeart,
wh
ere
the
cris
isb
egin
sin
Gre
ece
in2009
an
din
2010
inth
eoth
erG
IIP
Sco
untr
ies.
Fir
mco
ntr
olva
riab
les
incl
ud
eth
elo
gari
thm
of
tota
lass
ets,
leve
rage,
net
wort
h,
tan
gib
ilit
y,in
tere
stco
vera
ge
rati
o(n
otin
Col
um
n(3
)),
and
EB
ITD
A/t
otal
asse
tsan
dfo
rth
eca
shre
gre
ssio
na
firm
’sca
shfl
owan
dca
pit
al
exp
end
iture
s.A
llfi
rm-l
evel
contr
olva
riab
les
are
lagg
edby
one
per
iod
.A
llva
riab
les
are
defi
ned
inT
ab
leI.
All
regre
ssio
ns
incl
ud
efi
rm,
ind
ust
ry-c
ou
ntr
y-y
ear,
and
fore
ign
ban
kco
untr
y-y
ear
fixed
effec
ts,
asw
ell
as
all
firm
-lev
elco
ntr
ols
.S
tan
dard
erro
rsare
adju
sted
for
het
erosc
edast
icit
yan
dcl
ust
ered
atth
efi
rm-l
evel
.S
ign
ifica
nce
leve
ls:
*(p
<0.1
0),
**
(p<
0.0
5),
***
(p<
0.0
1).
54
Table IX - Active Channel: Risk Shifting
(1) (2) (3) (4) (5) (6)Net Debt ∆Cash Int. Cov. Emp. Growth CAPX Sales Growth
Panel A: Leverage
High Leverage GIIPS in Crisis -0.032* -0.003 -0.028** -0.036** -0.049** -0.040**(-1.94) (-0.73) (-2.51) (-2.13) (-2.13) (-2.01)
GIIPS in Crisis -0.015 0.006 -0.012** -0.025** -0.031* -0.019(-1.17) (1.41) (-2.37) (-2.00) (-1.70) (-1.29)
High Leverage GIIPS in Crisis*Cash Flow 0.011**(2.06)
GIIPS in Crisis*Cash Flow 0.003(0.68)
R2 0.554 0.461 0.431 0.430 0.594 0.500N 4339 3918 4535 3695 4246 4115
Panel B: Rating
Low Rating GIIPS in Crisis -0.026* -0.004 -0.019* -0.032** -0.044** -0.063***(-1.68) (-0.70) (-1.68) (-2.01) (-1.99) (-2.72)
GIIPS in Crisis -0.033*** 0.008* -0.015*** -0.025* -0.037** -0.032**(-2.72) (1.89) (-2.97) (-1.94) (-2.10) (-2.10)
Low Rating GIIPS in Crisis*Cash Flow 0.017*(1.83)
GIIPS in Crisis*Cash Flow 0.008**(2.54)
R2 0.553 0.464 0.424 0.430 0.596 0.502N 4339 3918 4535 3695 4246 4115
Table IX presents firm-level regressions. The dependent variables are net debt, the change in cash holdings,interest coverage ratio, employment growth, investments, and sales growth, respectively. The sample consistsof all firms in the intersection of DealScan and Amadeus and located in: Greece, Italy, Ireland, Portugal,Spain (GIIPS countries) or Germany, France, U.K. (non-GIIPS countries), with a relationship with a bankthat was part of the EBA stress tests. GIIPS measures the fraction of syndicated loans provided by GIIPSbanks and High Leverage GIIPS (Low Rating GIIPS ) the fraction provided by high leverage (low rating)GIIPS banks. A bank is considered highly leveraged if its total equity/total assets ratio is below the samplemedian in 2009 (separate median split for GIIPS and non-GIIPS banks) (Panel A) and it is consideredto have a low rating if its median rating is A+ or lower in 2009 (Panel B). GIIPS in Crisis measures thefraction of syndicated loans provided by GIIPS banks and High Leverage GIIPS in Crisis (Low Rating GIIPSin Crisis) the fraction provided by high leverage (low rating) GIIPS banks that are incorporated in a crisiscountry in year t, where the crisis begins in Greece in 2009 and in 2010 in the other GIIPS countries.. Firmcontrol variables include the logarithm of total assets, leverage, net worth, tangibility, interest coverage ratio(not in Column (3)), and EBITDA/total assets and for the cash regression a firm’s cash flow and capitalexpenditures. All firm-level control variables are lagged by one period. All variables are defined in TableI. All regressions include firm, industry-country-year, and foreign bank country-year fixed effects, as well asall firm-level controls. Standard errors are adjusted for heteroscedasticity and clustered at the firm-level.Significance levels: * (p < 0.10), ** (p < 0.05), *** (p < 0.01).
55
Table X - Active Channel: Moral Suasion
(1) (2) (3) (4) (5) (6)Net Debt ∆Cash Int. Cov. Emp. Growth CAPX Sales Growth
Panel A: Intervened Banks
GIIPS Gov. Intervention in Crisis -0.004 -0.006* -0.004 0.002 0.001 -0.011(-0.44) (-1.95) (-0.3) (0.21) (0.04) (-0.62)
GIIPS in Crisis -0.032** 0.009* -0.012** -0.032** -0.040** -0.038**(-2.55) (1.95) (1.96) (-2.44) (-2.26) (-2.37)
GIIPS Gov. Intervention in Crisis*Cash Flow 0.009(1.54)
GIIPS in Crisis*Cash Flow 0.007*(1.83)
R2 0.552 0.463 0.426 0.429 0.593 0.500N 4339 3918 4535 3695 4246 4115
Panel B: Government Ownership
High Fraction Gov. Own. GIIPS in Crisis 0.003 0.000 0.006 0.011 0.012 0.015(0.31) (0.03) (0.83) (1.22) (0.81) (1.33)
GIIPS in Crisis -0.024* 0.006 -0.030*** -0.031** -0.044** -0.046***(-1.74) (1.33) (-3.06) (-2.14) (-2.36) (-2.80)
High Fraction Gov. Own. GIIPS in Crisis*Cash Flow -0.000(-0.04)
GIIPS in Crisis*Cash Flow 0.010***(2.69)
R2 0.554 0.463 0.428 0.430 0.593 0.500N 4339 3918 4535 3695 4246 4115
Panel C: Government Board Seats
High Fraction Gov. Board GIIPS in Crisis 0.012 -0.004 -0.002 0.011 0.012 0.015(1.10) (-1.23) (-0.26) (1.04) (0.83) (1.21)
GIIPS in Crisis -0.032** 0.006 -0.018** -0.026* -0.037** -0.048***(-2.47) (1.40) (-2.59) (-1.80) (-2.16) (-3.02)
High Fraction Gov. Board GIIPS in Crisis*Cash Flow -0.002(-0.41)
GIIPS in Crisis*Cash Flow 0.012***(3.31)
R2 0.553 0.462 0.424 0.431 0.593 0.500N 4339 3918 4535 3695 4246 4115
Table X presents firm-level regression results. The dependent variables are net debt, the change in cashholdings, interest coverage ratio, employment growth, investments, and sales growth, respectively. Thesample consists of all firms in the intersection of DealScan and Amadeus and located in: Greece, Italy, Ireland,Portugal, Spain (GIIPS countries) or Germany, France, U.K. (non-GIIPS countries), with a relationship witha bank that was part of the EBA stress tests. GIIPS is the fraction of syndicated loans provided by GIIPSbanks; Gov. Intervention by GIIPS banks that received government support; High Fraction Gov. Own. byGIIPS banks with an above median government ownership; High Fraction Gov. Board GIIPS by GIIPSbanks with an above median fraction of government affiliated directors. GIIPS in Crisis is the fractionof syndicated loans provided by GIIPS banks; Gov. Intervention GIIPS in Crisis by GIIPS banks thatreceived government support; High Fraction Gov. Own. GIIPS in Crisis by GIIPS banks with an abovemedian government ownership; High Fraction Gov. Board GIIPS in Crisis by GIIPS banks with an abovemedian fraction of government affiliated directors, that are incorporated in a crisis country in year t, wherethe crisis begins in Greece in 2009 and in 2010 in the other GIIPS countries.. Firm control variables includethe logarithm of total assets, leverage, net worth, tangibility, interest coverage ratio (not in Column (3)), andEBITDA/total assets and for the cash regression a firm’s cash flow and capital expenditures. All firm-levelcontrol variables are lagged by one period. All variables are defined in Table I. All regressions include firm,industry-country-year, and foreign bank country-year fixed effects, as well as all firm-level controls. Standarderrors are adjusted for heteroscedasticity and clustered at the firm-level. Significance levels: * (p < 0.10),** (p < 0.05), *** (p < 0.01).
56
Figure 1. Real Effects - Entire Sample
-.025
0
.025
.05
-2 -1 0 1 2
Panel A: Employment Growth
.075
.1
.125
.15
.175
.2
-2 -1 0 1 2YEAR
Panel B: Investment
-.025
0
.025
.05
.075
-2 -1 0 1 2YEAR
High GIIPS Bank Dep. Low GIIPS Bank Dep.
Panel C: Sales Growth
YEAR
Figure 1 shows employment growth rates (Panel A), capital expenditures as a fraction of tangible assets(Panel B), and sales growth rates (Panel C) for firms with high (red solid line) and low (blue dashed line)GIIPS Bank Dependence in the pre-crisis period (years -2 and -1) and the crisis period (starting in year0). We consider all loans in DealScan to firms located in: Greece, Italy, Ireland, Portugal, Spain, Germany,France, or U.K. We restrict the sample to firms with financial information in Amadeus.
57
Figure 2. Real Effects - Non-GIIPS Firms without GIIPS or other non-EU Subsidiaries
-.025
0
.025
.05
-2 -1 0 1 2YEAR
Panel A: Employment Growth
.075
.1
.125
.15
.175
.2
-2 -1 0 1 2YEAR
Panel B: Investment
-.05
-.025
0
.025
.05
.075
-2 -1 0 1 2YEAR
High GIIPS Bank Dep. Low GIIPS Bank Dep.
Panel C: Sales Growth
Figure 2 shows employment growth rates (Panel A), capital expenditures as a fraction of tangible assets(Panel B), and sales growth rates (Panel C) for firms in Germany, France, or U.K. with high (red solid line)and low (blue dashed line) GIIPS Bank Dependence in the pre-crisis period (years -2 and -1) and the crisisperiod (starting in year 0) that do not have subsidiaries in Greece, Italy, Ireland, Portugal, Spain, or othernon-EU countries. We restrict the sample to firms with financial information available in Amadeus.
58
Figure 3. Evolution of Sovereign Debt Holdings - All Banks
0
5
10
15
20
25
30
35
.01
.02
.03
.04
.05
.06
.07
.08
.09
2009 2010 2011 2009 2010 2011
Non-GIIPS Banks GIIPS Banks
Panel A: GIIPS Sovereign Debt Exposure (% Bank Assets)
0
5
10
15
20
25
.01
.02
.03
.04
.05
.06
.07
.08
.09
2009 2010 2011 2009 2010 2011
Non-GIIPS Banks GIIPS Banks
Holdings Country CDS-weighted Holdings
Panel B: Domestic Sovereign Debt Exposure (% Bank Assets)
Figure 3 shows the banks’ aggregated GIIPS (Panel A) and domestic (Panel B) sovereign bondholdings (solidblue line, left axis, as a fraction of total assets) and the banks’ aggregated GIIPS (Panel A) and domestic(Panel B) sovereign bondholdings multiplied by the CDS spread of the respective GIIPS country (dashedred line, right axis, as a fraction of total assets). GIIPS banks comprise all banks headquartered in Greece,Italy, Ireland, Portugal, or Spain. Non-GIIPS banks consist of banks headquartered in France, Germany, orthe U.K. Sovereign bondholding data are from the EBA. We compile total assets from SNL Financial andCDS spreads from Datastream. CDS spreads are measured at the end of the preceding year.
59
Figure 4. Evolution of Domestic Sovereign Debt Holdings - GIIPS Banks
051015202530354045505560657075
.01
.02
.03
.04
.05
.06
.07
.08
.09
2009 2010 2011 2009 2010 2011
low leverage GIIPS bank high leverage GIIPS bank
Panel A: Domestic Sovereign Debt Exposure (% Bank Assets)
051015202530354045505560657075
.01
.02
.03
.04
.05
.06
.07
.08
.09
2009 2010 2011 2009 2010 2011
high rating GIIPS bank low rating GIIPS bank
Holdings Country CDS-weighted Holdings
Panel B: Domestic Sovereign Debt Exposure (% Bank Assets)
Figure 4 shows the banks’ aggregated domestic sovereign bondholdings (solid blue line, left axis, as a fractionof total assets) and these holdings multiplied by the CDS spread of the banks’ home countries (dashed red line,right axis, as a fraction of total assets). High (low) leverage GIIPS banks comprise all banks headquarteredin Greece, Italy, Ireland, Portugal, or Spain that have a below (above) median total equity to total assetsratio (Panel A). Low (high) rating GIIPS banks comprise all banks headquartered in a GIIPS country thathave a rating of A+ or lower (AA- or better) (Panel B). We compile total assets from SNL Financial andCDS spreads from Datastream. CDS spreads are measured at the end of the preceding year.
60